Sex-specific dispersal patterns of the threatened Northern River Shark

Sex-specific dispersal patterns of the threatened Northern River Shark | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sex-specific dispersal patterns of the threatened Northern River Shark Floriaan Devloo-Delva, Peter Kyne, James Marthick, Michael Grant, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5955537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Quantifying genetic connectivity between populations of a species is important to understand its conservation and management needs. Genetic connectivity implies gene flow among discrete populations occurring via the dispersal of individuals outside their population of origin, followed by reproduction. This process can be biased between sexes, with increasing evidence of sex-biased dispersal in elasmobranchs (sharks and rays). In this study we assessed the historical and contemporary connectivity of the threatened Northern River Shark ( Glyphis garricki ) using mitochondrial genomes, complemented with a genealogical framework based on close-kin relationships. Almost all of the 11 sampling locations in five regions across their known range formed a distinct breeding unit, with those locations in close geographical proximity sharing more kin. Close-kin results, based on cross-river half siblings and mitochondrial haplotypes, suggested that males had higher dispersal rates (63 % [23–68 %]), compared to females (10 % [5–20 %]). High fixation indices (global ΦST = 0.71) and a large proportion of same-river maternal half siblings (89 % [79–95 %]) indicated that females returned to their river of origin for pupping (i.e. natal philopatry on historical and contemporary timescales). By including full mitochondrial genome data, close-kin methods can detect sex-biased contemporary connectivity over the last couple generations. This is crucially important for the framing of conservation and management actions, and could require a sex-specific assessment of threats. Biological sciences/Genetics/Population genetics Biological sciences/Ecology/Molecular ecology Biological sciences/Evolution/Population genetics Glyphis garricki close-kin SNP mitogenome philopatry sex-biased dispersal Figures Figure 1 Figure 2 Figure 3 Introduction Information on population structure and genetic, or reproductive, connectivity is crucial to assist the conservation and management of threatened species. Small, isolated populations may be susceptible to local extinction, whereas well-connected populations can maintain sufficient genetic variability and fitness (Caughley 1994; Frankham et al. 2017; Ralls et al. 2020). Nonetheless, biases in sex ratios and/or dispersal rates among sexes can have implications for the sustainability of seemingly well-connected populations. (Caughley 1994). Recently-established small populations may have lower arrivals from one sex and could be negatively impacted through elevated levels of inbreeding (Bonte et al. 2012). In addition, the intensity, duration, and variety of threats can differ greatly between the dispersing and non-dispersing (i.e. resident or philopatric) sex (Lucas et al. 1994). Philopatry and sex-biased dispersal are important traits for conservation and resource management that both describe the individual-level dispersal behaviour of a species within and among populations, resulting in population-scale dynamics (Dobson 2013; Greenwood 1980). We define philopatry as the seasonal return migration of reproducing individuals to their natal site (natal philopatry) or region (regional philopatry), and sex-biased dispersal as the asymmetric reproductive movement rates of females over males (or vice versa) between distinct populations (Chapman et al. 2015; Phillips et al. 2021). Dispersal is typically undertaken by both sexes, but dispersal rate and distance can differ between females and males (Pusey 1987). For instance, Blundell et al. (2002) illustrated that male River Otters ( Lontra canadensis ) dispersed more frequently than females, but only to nearby areas, whereas females travelled greater distances. In elasmobranchs (sharks and rays), female philopatry and male-biased dispersal have been recorded, to date, in 62 and 25 species, respectively (Chapman et al. 2015; Flowers et al. 2016; Phillips et al. 2021). Many methods are used to infer philopatry and sex-biased dispersal (Goudet et al. 2002; Prugnolle and de Meeus 2002). By following the matrilineal connectivity with mitochondrial DNA (mtDNA), studies have shown whether females historically revisited the same breeding or pupping grounds (i.e. female philopatry). The comparison of mtDNA with biparentally- or paternally-inherited nuclear DNA (nuDNA) allows the ‘indirect’ inference of historical sex-biased gene flow when either nuDNA or mtDNA shows a different pattern of structuring between populations. However, secondary contact of two previously separated populations can lead to a similar mito-nuclear discordance (Toews and Brelsford 2012). A novel method to measure contemporary ‘reproductive’ dispersal from juveniles using genomic data was proposed by Bravington et al. (2016). This reproductive dispersal can be measured ‘directly’ or ‘indirectly’ by analysing the spatial distribution of parent-offspring pairs (POPs) or half-sibling pairs (HSPs), respectively. The latter was applied by Feutry et al. (2017) and Patterson et al. (2022) to study the Speartooth Shark ( Glyphis glyphis ); a euryhaline species known to occur in a limited number of macrotidal tropical rivers of northern Australia and southern Papua New Guinea (PNG; Pillans et al. 2009; White et al. 2015). They identified 121 cross-cohort (i.e. born in different years) HSPs using single nucleotide polymorphism (SNP) markers. Data from the full mitochondrial genome (mitogenome) indicated that all 18 cross-cohort, cross-river (i.e. sampled from different rivers) HSPs were likely paternally-related (i.e. different mtDNA haplotype), indicating that their fathers moved between populations (i.e. contemporary male-biased dispersal). The Northern River Shark ( Glyphis garricki ) is a recently described euryhaline shark known from a small number of rivers and estuaries of northern Australia and southern PNG (Compagno et al. 2008; Feutry et al. 2020; Kyne et al. 2021b). It has prolonged use of estuarine environments within its life history, including their use for pupping and nursery areas, yet the location of mating aggregations are currently unknown (Grant et al. 2023; Pillans et al. 2009). The main threats to this species include fishing (target and bycatch) and habitat modification (Kyne et al. 2021c). It is globally listed as Vulnerable on the IUCN Red List of Threatened Species (Kyne et al. 2021c), and listed as Endangered on Australia’s Environment and Biodiversity Protection Act (EPBC Act 1999). Feutry et al. (2020) identified five distinct genetic populations across its distribution using 1,700 SNP markers: King Sound and Cambridge Gulf in Western Australia, Daly River and Van Diemen Gulf (VDG) in the Northern Territory, and southern PNG. The study showed that the King Sound population exhibited low genetic diversity. It also found both low historical and contemporary genetic connectivity among the populations and sampling locations (within populations) based on the number of migrants per generation ( Nm ) and kinship distribution. Whether there is a sex bias in dispersal among populations is currently unknown. In this study, we investigate the maternal evolutionary history and assess the possibility of female philopatry and male-biased dispersal in G. garricki with three different types of data that capture population dynamics at different timescales. First, we evaluate the mitochondrial genetic variation among regions to detect historical female philopatry and demographic events. Second, we compare full mitogenome data, which have a higher resolution than conventional regions (e.g. control region; Feutry et al. 2014), against the SNP data described by Feutry et al. (2020) to infer any historical demographic differences between females and males, putatively driven by unequal gene flow or difference in effective population size. Third, we use mtDNA data to infer the paternal or maternal relationship between half siblings to assess contemporary philopatry and sex-biased dispersal at a fine spatial scale. Materials And Methods Sample collection and DNA extraction Between 2012 and 2016, a total of 379 G. garricki tissue samples were collected from 11 rivers, creeks, large marine embayments, or estuaries (hereafter referred to as sampling locations) in five different regions covering the entire known geographical range of the species (Fig. 1; Feutry et al. 2020; Kyne et al. 2021b; White et al. 2015). The same tissue samples as Feutry et al. (2020) were used in this study, with the exception of six PNG samples that were collected by Grant et al. (2021). Each shark was measured, sexed, and sampled for genetic material before it was released at the site of capture. The total length (TL) of all sharks ranged from 52 to 182 cm; most sharks were juveniles or sub-adults, with only 20 males >141 cm TL (sexually mature i.e. possessing calcified claspers; Feutry et al. 2020). Sexual maturity in female sharks cannot always be assessed externally, but seven females >153 cm TL were assumed to be mature based on the established male size-at-maturity (Feutry et al. 2020; Pillans et al. 2009). Genomic DNA was extracted following the standard protocol of the DNeasy Blood and Tissue kit (Qiagen Inc., Valencia, California, USA). Mitogenome amplification and sequencing The full mitochondrial genome was amplified with two primer pairs (A and B fragments; Supplementary material section 1.2), for all but eight samples that did not amplify. For these samples, primers that target quarter fragments of the mitogenome were designed (A1, A2, B1, and B2 fragments; Supplementary material section 1.2). Polymerase chain reactions (PCR) were performed in 30 μL reactions, following the standard proofreading Takara LA Taq protocol (Takara, Otsu, Shiga, Japan). PCR conditions were set to 1 min at 94°C for initial denaturation; then 40 cycles of denaturation (94°C, 30 s), annealing (55°C, 30 s), and extension (68°C, 10 min); concluding with a 10 min extension at 72°C. PCR products were cleaned following the Agencourt AMPure XP magnetic bead protocol (Beckman Coulter Inc., Indianapolis, Indiana, USA). Amplicons were quantified with a NanoDrop 8000 Spectrophotometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA) and the purified A and B fragments were pooled at equimolar concentration. Subsequently, these amplicons were simultaneously fragmented and barcoded with the Nextera XT DNA Sample Preparation kits and 96 sample Nextera Index kits (Illumina, San Diego, California, USA). The libraries were quantified with the Qubit dsDNA BR assay kit (Life Technologies, Carlsbad, California, USA) and normalized. Libraries were then pooled and sequenced on a Miseq desktop sequencer using the 2x250 bp paired-end reads MiSeq reagent kit v2 (Illumina, San Diego, California, USA). Mitogenome assembly and alignment Demultiplexed fastq files were imported into Geneious prime software v2021.2.2 (Biomatters Ltd., Auckland, New Zealand), and the reads were paired. The Nextera adapters were trimmed and the reads were quality trimmed at a phred score <20 for a Kmer of 20 using the BBDuk tool as implemented in Geneious. Reads shorter than 50 bp after trimming were discarded from subsequent analyses. Reads for each individual were then mapped onto a previously published reference sequence (Feutry et al. 2015) using the ‘Map to Reference’ tool in Geneious with the ‘high sensitivity’ parameters and 10 iterations. The majority rule consensus (>50 % of mapped reads for any single mutation, insertion, or deletion) for each shark was exported. In addition to the 379 samples, we obtained another six G. garricki mitogenomes from the Alligator rivers from NCBI Genbank (accession numbers: KF646786, KT698042, KT698044, KT698053, KT698059, NC_023361; Feutry et al. 2015; Li et al. 2015). All mitogenome sequences were aligned with the ‘multiple align’ tool and the MUSCLE algorithm (Edgar 2004). Genetic variation and haplotype analysis The mitogenome alignment and raw nuclear SNP data from Feutry et al. (2020) were imported into R 4.4.0 (R Core Team 2024) using the apex v1.0.6 and dartRverse v1.0.2 packages (Gruber et al. 2018; Jombart et al. 2017; Mijangos et al. 2022). Both mtDNA and nuclear SNP datasets were filtered so that they contained the same individuals, except the PNG mtDNA data that contained six different samples (Supplementary material). Nucleotide diversity (π), theta based on segregating site (θ), haplotype diversity (h), and haplotype networks were calculated with the pegas v1.3 package (Paradis 2010). Mitochondrial diversity estimates were compared against the nuclear DNA results from Feutry et al. (2020). Genetic differentiation and historical demography A global Analysis of Molecular Variance (AMOVA) was performed with pegas (10,000 permutations) to detect population differentiation among the five regions and 11 sampling locations within regions. Fixation indices (Φ ST ) were calculated with 10,000 permutations between sampling locations and between regions with the ‘popStructTest’ function in the strataG v2.5.0.1 package (Archer et al. 2017). We further compared the mitochondrial Φ ST values against the nuclear F ST and kinship results from Feutry et al. (2020). Kinship analyses Kin relationships in VDG were identified by Feutry et al. (2020) based on 379 individuals. We re-analysed the half sibling pairs in light of their maternal mtDNA relationship, year of birth, and spatial distribution within and between six sampling locations to infer sex-specific connectivity at a contemporary timescale (see Patterson et al. 2022). Adult dispersal can also be directly observed (i.e. by sampling the adult that moved) from parent-offspring pairs that were distributed between sampling locations, where we assumed that the oldest individual (i.e. parent) dispersed. The occurrence of cross-river full-sibling pairs (FSPs) was used to examine the incidence of juvenile dispersal. This is expected to be minimal based on the close association to nursery habitat, limited linear extent of river occupancy and the salinity preference of juvenile euryhaline sharks (Grant et al. 2023; Pillans et al. 2009). Sex-specific adult dispersal was inferred with an indirect approach (i.e. without sampling adults) by comparing the mtDNA haplotypes of HSPs. First, cross-cohort (i.e. born in different years), cross-river HSPs inform if parents moved between locations between breeding seasons. By comparing the mtDNA haplotypes of each pair (h1=h2 or h1≠h2), we can infer which parent, the mother or the father, is more likely to have moved between sampling locations. Sex-specific dispersal rates were calculated by comparing number of mothers or fathers that moved between rivers versus the ones that returned to the same river between breeding seasons. Alternatively, cross-cohort, same-river HSPs reveal how likely mothers and fathers are to return to the same river between breeding seasons (i.e. natal philopatry). Philopatry rates were calculated by comparing number of mothers or fathers that returned to the same river versus the ones that moved between rivers between breeding seasons. Additionally, the null hypothesis of ‘no maternal/paternal philopatry’ was statistically tested with an approximate likelihood ratio (Δ) test and randomising same and cross-river HSPs over 10,000 permutations, developed by Feutry et al. (2017). This test took the mtDNA haplotype frequencies into account to calculate the likelihood that maternally/paternally-related HSPs are more likely to occur in the same river. This likelihood approach is important when several haplotypes are common in the population. Each estimate of sex-biased dispersal, female philopatry and male philopatry was given a range of uncertainty by assuming that a HSP with the same haplotype might be paternally related if the frequency of the haplotype was higher than 50% in the river of collection. Lastly, since sample size influences the number of kin pairs found (Bravington et al. 2016), we corrected the number of HSPs by the number of pairwise comparisons performed (HSPcorr). For each sampling locations in VDG, the ratio of ‘HSPcorr within’ over the sum of ‘HSPcorr between’ (HSPcorr within /∑HSPcorr between ) was calculated to provide a non-gender specific estimate of philopatric and dispersive behaviours. Specifically, a ratio of >1 indicated that more individuals bred with individuals from the same location than they did with individuals from other locations (i.e. stronger philopatric behaviour), and conversely a ratio of <1 implied higher connectivity. Length-at-age function The connectivity inference based on the spatial distribution of kin relied on the fact that we could assign each individual to the year it was born with a reasonable amount of certainty. No age-and-growth studies have been performed on G. garricki . There are limited opportunities to obtain such data as G. garricki is a protected species in Australia and ageing of elasmobranchs is commonly estimated by examining growth bands in large sample sizes of vertebrae. In order to assign the age cohorts, Bravington et al. (2019) fitted a von Bertalanffy growth function to 34 recaptured sharks with a maximum size of 155 cm TL and a maximum recapture interval of two years: Where L t is the TL at capture (in cm), L 0 is the length at birth (50.0 cm), L ∞ is asymptotic length (154.7 cm), K is the growth coefficient (0.139 year -1 ), and t is the age (in years). While limitations of the growth function are acknowledged (e.g. L ∞ is likely underestimated, leading to an overestimation of K ), age cohort assignment for size classes closer to L 0 (i.e. juveniles) will be less prone to error as these age cohorts will capture a wider length range. However, larger individuals approaching L ∞ will have an exponentially increasing likelihood of falling in their own age cohort. Because the majority of specimens used were small (juveniles or subadults 154.7 cm TL (i.e. L ∞ ) could not be assigned to age cohorts and were excluded from this part of the analysis. We used the full sibling pairs (FSP) from the same river and caught in the same time period (2 weeks apart) to calculate a fixed standard deviation of length-at-age (0.43 cm) to the mean L t of each cohort (Supplementary material section 10.2.2). In addition, an extra 0.5 year was added to the upper and lower ranges of t for each age cohort to account for instances where catch dates were not aligned with the austral summer pupping season (e.g. cohort 1 t = 0–0.5, cohort 2 t = 0.5–1.5 etc). This conservative approach aimed to capture the variable range of lengths that may occur in each age cohort resulting from varied individual growth rates and birth sizes. Results Mitogenome assembly and alignment The full mitogenomes of 379 samples were sequenced with an average of 98,360 reads sequenced per sample. Two samples from the South Alligator River (VDG) had low coverage (<1,000 mapped reads) and were omitted from further analyses. Overall, the mitogenome length was 16,702–16,703 bp and consisted of 26 polymorphic sites across the remaining 383 sharks (including the six mitogenome sequences from NCBI). These included two insertions in tRNA-Tyr (-/T, n = 3) and Control Region (-/A, n = 28), and one deletion in tRNA-Cyt (G/-, n = 1). One region between tRNA-Pro and the Control Region consistently had low coverage (<100 mapped reads), most likely indicating the presence of a secondary structure. This may explain why eight samples did not amplify well and had to be amplified in quarter sections (Supplementary material section 1.2). All new G. garricki sequences were uploaded to NCBI GenBank (accession numbers: MW652871–MW653247). Genetic variation and haplotype analysis The average π for the 383 aligned sequences was 0.000095 (variance, σ 2 < 0.000001). Most variable sites had low diversity (π = 0.02–0.1), but three sites had π = 0.25–0.5 (Supplementary material section 4.1). The 383 sequences resulted in 26 haplotypes with a haplotype diversity of 0.7670 (σ 2 = 0.000233). The diversity indices per region and sampling location are summarised in Table 1 and Supplementary material sections 4.2 and 4.3, respectively. Here, we see that Cambridge Gulf, specifically West Cambridge Gulf, had the highest π and King Sound had the lowest. Van Diemen Gulf had the highest haplotype diversity (h = 0.659). This pattern becomes most obvious when illustrated in a haplotype network (Fig. 2; Supplementary material section 5). Eight haplotypes were singletons. All but two haplotypes were private to a single geographical region (Supplementary material section 4.3), and the haplotypes from PNG and two haplotypes from Cambridge Gulf were most distant (4–5 mutations). Other than these haplotypes, the network mainly shows a signal of expansion with one central haplotype (HT25) and the other haplotypes spreading out by one mutation at a time. Genetic differentiation and historical demography Overall, the AMOVA showed that the differentiation among regions and sampling locations within regions was highly significant (Φ ST = 0.710; p < 0.0001; Supplementary material section 6.1). Specifically, most high pairwise Φ ST values (0.77–0.94) were observed between PNG and all other sampling locations, except for West Cambridge Gulf (Table 2). The lowest Φ ST was observed between close sampling locations, such as within VDG (e.g. the Alligator rivers). Interestingly, some sampling locations within VDG (East Alligator River and Sampan Creek) appeared less differentiated from King Sound than geographically closer regions (Daly River and Cambridge Gulf). Kinship analyses In Van Diemen Gulf, Feutry et al. (2020) identified 4 parent-offspring pairs (POPs), 34 full-sibling pairs (FSPs), and 130 half-sibling pairs (HSPs) from 108 811 pairwise comparisons (Table 3 and Table 4). These pairs could be merged into 73 family groups (208 unique individuals); 43 groups consisted of only single pairs (POP, FSP or HSP), but 30 contained multiple pairs per group (i.e. a combination of POPs, FSPs, and HSPs; Supplementary material section 10.1). The mitogenome of eight samples could not be amplified or had a low sequencing coverage, which resulted in 12 HSPs with missing haplotype information. All father-offspring pairs had different haplotypes and all FSPs had a matching haplotype. Twenty-six and 101 HSPs had non-matching and matching haplotypes, respectively (Table 3; Supplementary material section 10.3). Overall, 54 same-cohort and 77 cross-cohort HSPs were identified. Eight same-river HSPs and two cross-river HSPs could not be assigned to a cohort due to missing length data or a size that was too large (i.e. >154.7 cm TL) for the growth function. All POPs were assigned to different cohorts and all but one FSPs were assigned to the same cohort, thus indicating that the growth function can assign individuals to an approximate cohort. One POP was distributed between the Wildman and South Alligator Rivers; the other three POPs were found between the East Alligator and South Alligator Rivers (Fig. 3). All FSPs were juveniles or sub-adults (<141 cm TL) and all, except one, were found within the same river. Sixteen cross-cohort, cross-river HSPs were also identified, of which 10 had different haplotypes and six had matching haplotypes (Table 3). However, of the latter six HSPs, three had very common haplotypes (HT06, HT13, and HT25; frequencies > 50 %; Table 3). For example, of the 16 cross-cohort, cross-river HSPs, four were distributed between the Adelaide River and the other rivers in VDG. Three out of the four pairs exhibited different haplotypes and were most likely paternally-related (Fig. 3), which would suggest that the father dispersed. Further, 56 cross-cohort HSPs shared a haplotype; six of these were caught in different rivers. Of the 56 HSPs with the same haplotype, 30 pairs (including three cross-river) shared a haplotype that was common in the sampling location (> 50 %; Table 3). A formal likelihood ratio test showed that HSPs with the same haplotype are more likely to be found in the same river than across river (Δ = 14.195, p < 0.0001), suggesting contemporary female philopatric behaviour. Conversely, male philopatry was not supported (Δ = 0.008, p = 1.000; Supplementary material section 10.3.3). When correcting the number of HSPs for the number of pairwise comparisons performed (Supplementary material section 10.4), we found that two sampling locations in VDG (Adelaide and Wildman Rivers) showed a stronger philopatric signal (>1). In contrast, we saw that Sampan Creek and the East Alligator River shared more kin between sampling locations, than retained within (>1). The West Alligator and South Alligator Rivers had an approximately equal ratio of same-river and cross-river kin (~1). Discussion Our study applied three different types of data to assess fine-scale population demography at three different timescales. The results provide new information on historical maternal population structure, as well as contemporary sex-specific dispersal of G. garricki , using the close-kin results from Feutry et al. (2020), supplemented with new whole mitogenome data. Specifically, the mitogenome results provide evidence of historical colonisation events and range expansion, as well as secondary contact between two separated lineages. Based on the 177 kin pairs, we observed that 63 % (10 out of 16) of the cross-cohort, cross-river half-sibling pairs (HSPs) are paternally-related and 89 % (50 out of 56) of the same-river, cross-cohort HSPs are maternally-related, indicating high male dispersal rates and female philopatry respectively. Mitochondrial results also show that each sampling location has significant differentiation. Combined with high nuclear SNP fixation indices (Feutry et al. 2020), this suggests strong historical population structure at a very fine spatial scale. Maternal demographic history Clear mitochondrial differentiation between all sampling location was detected in this study and is consistent with the lack of connectivity reported by Feutry et al. (2020). However, low Φ ST between the Ord and Daly rivers is most likely explained by ongoing gene flow or the retention of ancestral haplotypes in recently diverged populations (incomplete lineage sorting; Toews and Brelsford 2012). This concurs with results from Feutry et al. (2020) that G. garricki started its range expansion recently in the Gulf of Carpentaria (Fig. 1) and subsequently expanded both westwards and north-eastwards. In addition, the mitogenome analyses revealed that PNG was most different from all sampling locations. This would suggest that PNG and northern Australian sharks may have been separated for a long period of time with limited female gene flow. Results also show two haplotypes in the Cambridge Gulf that are more similar to PNG, indicating either secondary contact between sharks from Cambridge Gulf and PNG, or an unsampled (or extinct) population. This is substantiated by a similar mitogenome observation for the congeneric G. glyphis (Kyne et al. 2021a) and the clustering of G. garricki samples between PNG and Cambridge Gulf based on the nuclear SNP data (Feutry et al. 2020). Female philopatry and male-biased dispersal On an evolutionary timescale, we observe high pairwise Φ ST between most sampling locations. This suggests that females consistently return to the same river for breeding, which is most likely the site they were born (i.e. natal philopatry). Additionally, some disjuncture between mitochondrial and nuclear data is apparent. The mitogenome differentiation from King Sound to other regions is less pronounced than in the nuclear data from Feutry et al. (2020), with the lowest differentiation between King Sound and the East Alligator River. Such ‘mito-nuclear discordance’ is likely driven by range expansion towards King Sound with incomplete mtDNA lineage sorting (Toews and Brelsford 2012) and an accumulation of nuDNA mutations at the edge of a range expansion due to the effect of drift on a small and recently founded population (Feutry et al. 2020; Peischl et al. 2013).This is again supported by the low genetic diversity in King Sound as suggested by Thorburn and Morgan (2004). These demographic events will affect the non-recombining haploid mtDNA, and recombining diploid nuDNA, differently (Lawson Handley and Perrin 2007; Phillips et al. 2021) Nonetheless, the low mitochondrial diversity towards the edge of the range expansion (i.e. King Sound) indicates a low influx of genetically diverse females and/or a disproportional amount of male colonisation. Overall, the high and significant mitochondrial and nuclear fixation indices between most sampling locations would suggest that both males and females show historical philopatric behaviours. On a contemporary timescale based on kinship in Van Diemen Gulf, we see evidence that females are more likely to return to their river of birth for parturition (i.e. natal philopatry). This trans-generational female philopatry is inferred from the result that 50 out of 56 of the cross-cohort HSP sharing a mother (i.e. same mtDNA haplotype) were found in the same river. However, 30 of these 56 HSPs (including three cross-river) shared a haplotype that was common in the sampling location (frequencies > 50 %) and could represent paternally-related HSPs. Thus, the female bias in philopatric behaviour ranged between 79.3 % (23/29) and 94.3 % (50/53). Further, we observe a bias towards male dispersal, evidenced by the four father-offspring pairs across rivers and 10 out of 16 cross-river, cross-cohort HSPs that were paternally related (i.e. different haplotype). If we consider the 30 cross-cohort HSPs that share a haplotype with high frequency (potentially paternally-related), the amount of male dispersal ranged between 23.2 % (10/43) and 68.4 % (13/19). Previously, Feutry et al. (2017) and Patterson et al. (2022) demonstrated that male G. glyphis are more likely to disperse between the Adelaide River and Alligator rivers. In the current study, we showed a similar contemporary male-biased dispersal pattern for G. garricki between the Adelaide River and the other VDG rivers (75 % male bias). Yet, between more closely located VDG rivers, the sex bias was less pronounced. Overall, this information on female philopatry and male-biased dispersal adds to the growing theory of elasmobranch dispersal (Chapman et al. 2015; Flowers et al. 2016; Phillips et al. 2021). Connectivity over three different timescales In general, we see that the three different approaches (mtDNA, nuDNA, and kinship) reinforce each other. When pairwise fixation indices are high, few kin are found between sampling locations and vice versa. For example, kinship data suggest stronger philopatric behaviour in the Adelaide and Wildman Rivers, demonstrated by the higher fixation indices and the many kin pairs retained within these sampling locations. We also found high reproductive connectivity (i.e. low fixation indices and many cross-river HSPs) between spatially close rivers in VDG, such as Sampan Creek and the East Alligator River. The increased connectivity is not explained solely by geographic proximity (Supplementary material section 10.4). Low mating opportunities, food availability, or environmental fluctuations may be responsible for the relatively few same-river kin pairs in these sampling locations (Comins et al. 1980; Greenwood 1980; Hamilton and May 1977). On a few occasions we observed a discordance between marker types. Non-significant Φ ST , but significant F ST , were found between three pairwise comparisons. As mentioned before, this likely reflects the retention of ancestral polymorphisms in the mitochondrial genome, yet the statistical power to detect population structure of thousands of SNPs is also expected to be higher than a single mtDNA marker (Morin et al. 2009). Another conflict between methods was demonstrated by the many cross-river kin between the Alligator rivers as opposed to high and significant pairwise fixation indices. This could reflect that these adjacent sampling locations have only recently been connected. Overall, we show that the close-kin method, supplemented with mtDNA, is a valuable tool for defining fine-scale population structure, provided that sampling is spatially and temporally extensive with sufficient covariate data (Bravington et al. 2016; Patterson et al. 2022). Conservation implications As a euryhaline species, Glyphis garricki is susceptible to population decline due to exposure to both riverine and marine pressures (Grant et al. 2019; Pillans et al. 2009). Fishing, particularly commercial gillnetting, likely poses the largest threat to G. garricki in Australia (DCCEEW 2023; Kyne and Feutry 2017), while small-scale fisheries pose a severe threat in its PNG range (Amepou et al. 2024). This species is also inherently susceptible to habitat modification (such as changes to natural freshwater flow regimes) and degradation of riverine and coastal environments (Grant et al. 2019). Since G. garricki is ecologically confined to estuaries in early life stages (Grant et al. 2023; Pillans et al. 2009), its ability to migrate from unfavourable conditions to neighbouring estuaries via marine coastal waters may be restricted. The present results support this ecological confinement and susceptibility, and further highlight gene flow implications. Similar to Feutry et al. (2020), we see that the King Sound population has the lowest diversity and the lowest θ. This supports previous statements that any environmental or anthropogenic changes in this area will likely affect G. garricki (Morgan et al. 2011; Thorburn and Morgan 2004). Further, we confirmed that each sampling location within VDG forms a unique genetic unit, with high female philopatry and male-biased dispersal. This means that for isolated populations the immigration of males cannot compensate for the removal of local females, and if a river/estuary were to be impacted by increased levels of mortality, natural recovery would not be guaranteed. As such, protection at the smallest spatial scale is essential to ensure local viability of populations, while allowing male dispersal to maintain genetic connectivity. Collectively, given the relatively small population size estimates of G. garricki (Bravington et al. 2019), threats and their associated mortality rates need to be mitigated, as they may have disproportionate impacts on each sex. Conclusion This study provides the most comprehensive overview available on the spatial population structure of the threatened shark G. garricki . We investigated connectivity on both evolutionary and contemporary timescales. It provides novel insights into contemporary sex-specific dispersal using a close-kin framework, and shows that full mitogenomes can add a new dimension to the kinship analyses, as well as resolve important historical events, such as the connectivity between Cambridge Gulf and PNG. In addition, we found that females exhibit a strong philopatric behaviour and that mainly males disperse within Van Diemen Gulf. This study indicates that each sampling location should be managed as a separate unit, since gene flow is not uniform and females appear to return to the same river. Lastly, this study highlights the importance of a multi-method approach to provide crucial information for conservation and management of threatened species and their environment. Declarations Acknowledgements This work was supported by the Marine Biodiversity Hub, a collaborative partnership supported through funding from the Australian Government's National Environmental Science Program. This research was funded in part through an Ord River Research Offset grant through CSIRO. Floriaan Devloo-Delva was supported by a joint UTAS/CSIRO scholarship and the Quantitative Marine Science program. We thank the many Traditional Owners, assistants, and volunteers that assisted with field work. Conflicts of Interest : We note no conflict or competing interests among the authors in relation to the information provided in this manuscript. Data archiving Mitochondrial genomes are uploaded to NCBI GenBank (accession numbers: MW652871 - MW653247) and alignment, metadata and Rmarkdown are available from the CSIRO Data Access Portal: https://doi.org/10.25919/hpf0-d336 (Devloo-Delva et al. 2025). SNP genotypes and metadata from Feutry et al. (2020) are available at https://doi.org/10.5061/dryad.hqbzkh1ch Compliance with ethical standards Sharks were sampled under Northern Territory Fisheries Special Permits S17/3252 and S17/3364, Kakadu National Park Research Permit RK805, Western Australian Department of Fisheries Exemption No. 2630, Western Australian Department of Parks and Wildlife Permit SF010485, and Charles Darwin University Animal Ethics Committee Approval A11041. Samples from Papua New Guinea (PNG) were obtained opportunistically through observations of small-scale fisheries (Grant et al. 2021; White et al. 2015). Authors' contributions The study was designed by FD, PMK, and PF. Funding was secured by PMK, RDP, and TS. Samples were provided by PMK, MIG, GJJ, DLM, RDP, and WTW. Lab work was performed by FD, JRM, RMG, and PMG. The data was analysed by FD and PF. The manuscript was drafted by FD and all authors contributed to revising the manuscript. 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PLoS One 10 : e0140075. Tables Table 1: Genetic variation in the mitogenome (16,703 bp) of Glyphis garricki . Region/ sampling location n π H h θ σ 2 θ King Sound 14 0.000022 2 0.363 0.315 0.099 Cambridge Gulf 30 0.000122 5 0.453 2.524 1.171 West Cambridge Gulf 15 0.000194 4 0.667 2.768 1.993 Ord River 15 0.000016 2 0.133 0.615 0.246 Daly River 30 0.000036 6 0.526 1.262 0.437 Van Diemen Gulf 305 0.000066 12 0.659 1.749 0.390 Adelaide River 25 0.000044 6 0.523 1.324 0.548 Sampan Creek 30 0.000082 7 0.777 1.515 0.618 Wildman River 46 0.000058 6 0.493 1.143 0.372 West Alligator River 41 0.000017 2 0.139 0.467 0.129 South Alligator River 102 0.000060 5 0.563 0.771 0.185 East Alligator River 61 0.000064 7 0.737 1.282 0.396 Papua New Guinea 6 0.000040 3 0.600 0.876 0.468 Abbreviations: n, number of samples; π, nucleotide diversity; H, haplotype richness; h, haplotype diversity; θ, theta (where θ = 2N ef µ); and σ 2 θ , variance of theta. Table 2: Pairwise fixation indices per sampling location for Glyphis garricki . Below diagonal: mitochondrial DNA Φ ST (this study). Above diagonal: nuclear DNA F ST (Feutry et al. 2020). Non-significant results after Bonferroni correction (p > 0.0009) are underlined. Sampling locations were King Sound (KS), Cambridge Gulf (WC, West Cambridge Gulf; O, Ord River), Daly River, Van Diemen Gulf (A, Adelaide River; S, Sampan Creek; W, Wildman River; WA, West Alligator River; SA, South Alligator River; EA, East Alligator River), and Papua New Guinea (PNG). Φ ST \ F ST KS WC O D A S W WA SA EA PNG King Sound 0.297 *** 0.302 *** 0.290 *** 0.287 *** 0.274 *** 0.270 *** 0.271 *** 0.259 *** 0.268 *** 0.395 ** West Cambridge Gulf 0.337 *** 0.008 * 0.093 *** 0.122 *** 0.122 *** 0.120 *** 0.121 *** 0.122 *** 0.123 *** 0.156 ** Ord River 0.768 *** 0.243 * 0.096 *** 0.128 *** 0.128 *** 0.126 *** 0.126 *** 0.129 *** 0.128 *** 0.154 ** Daly River 0.671 *** 0.313 *** 0.060 * 0.091 *** 0.089 *** 0.088 *** 0.089 *** 0.089 *** 0.090 *** 0.180 *** Adelaide River 0.473 *** 0.469 *** 0.728 *** 0.702 *** 0.013 *** 0.015 *** 0.015 *** 0.015 *** 0.014 *** 0.174 ** Sampan Creek 0.178 ** 0.370 *** 0.543 *** 0.561 *** 0.233 ** 0.006 ** 0.004 *** 0.002 ** 0.001 * 0.170 *** Wildman River 0.392 *** 0.494 *** 0.653 *** 0.655 *** 0.491 *** 0.081 * 0.007 *** 0.008 *** 0.006 *** 0.171 *** West Alligator River 0.750 *** 0.638 *** 0.871 *** 0.822 *** 0.715 *** 0.294 *** 0.127 ** 0.005 *** 0.004 *** 0.173 *** South Alligator River 0.326 ** 0.526 *** 0.609 *** 0.618 *** 0.249 ** 0.036 0.140 ** 0.221 *** 0.002 *** 0.172 *** East Alligator River 0.126 ** 0.425 *** 0.545 *** 0.559 *** 0.222 *** 0.005 0.180 *** 0.365 *** 0.082 ** 0.172 *** Papua New Guinea 0.901 *** 0.574 *** 0.931 *** 0.893 *** 0.862 *** 0.767 *** 0.828 *** 0.938 *** 0.818 *** 0.798 *** *** p < 0.0001; ** p < 0.005; * p < 0.05 Table 3: Glyphis garricki kin pairs identified in Van Diemen Gulf by Feutry et al. (2020). The haplotype data (same ‘=HT’, different ‘≠HT’, or ‘missing) from this study allows the inference of philopatric behaviour and sex-specific connectivity. The number in parentheses indicates the half-sibling pairs with a matching haplotype, where the haplotype has a high frequency (<50 %) and could be paternally related. These numbers were used to estimate the minimum and maximum ranges of philopatry and dispersal rates. Father-offspring pairs Full sibling pairs Half sibling pairs Purpose =HT ≠HT Missing =HT ≠HT Missing =HT ≠HT Missing Same-river, same-cohort - - - 32 - - 35(25) 6 5 Litter size and multiple paternity Same-river, cross-cohort - - - 1 - - 50(27) 6 5 Philopatry* Female: 50/56 [23/29–50/53] Male: 6/16 [6/19–33/43] Same-river, missing-cohort - - - - - - 4(1) - 2 - Cross-river, same-cohort - - - 1 - - 4(0) 4 - Direct movement Cross-river, cross-cohort - 4 - - - - 6(3) 10 - Sex-specific connectivity* Male: 10/16 [10/43–13/19] Female: 6/56 [3/56–6/29] Cross-river, missing-cohort - - - - - - 2(1) - - - *This study focusses on philopatry and sex-specific connectivity. Table 4 is available in the Supplementary Files section. Additional Declarations There is no duality of interest Supplementary Files DevlooDelvaetal.Supplementarymaterial.pdf Supplementary material: R markdown containing all analyses performed for the present study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5955537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":415157853,"identity":"8f6c4a8a-2475-4e87-b055-be5b82d2d262","order_by":0,"name":"Floriaan 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1","display":"","copyAsset":false,"role":"figure","size":1750808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlyphis garricki\u003c/em\u003e sampling regions and sampling locations, from west to east: King Sound, Cambridge Gulf (WC, West Cambridge Gulf; O, Ord River), Daly River, Van Diemen Gulf (A, Adelaide River; S, Sampan Creek; W, Wildman River; WA, West Alligator River; SA, South Alligator River; EA, East Alligator River), and Papua New Guinea. Each pie chart is coloured according to the haplotypes in the sampling location, with the haplotype frequency provided for each river.\u003c/p\u003e","description":"","filename":"Fig.1SampleMap.png","url":"https://assets-eu.researchsquare.com/files/rs-5955537/v1/b0c60f0cf8251f7f72c670fe.png"},{"id":76454455,"identity":"4c42b388-79b2-4b42-82fb-b85b2b2bed29","added_by":"auto","created_at":"2025-02-17 10:22:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":364386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlyphis garricki\u003c/em\u003e haplotype network. The size of the circles is equivalent to the square root of the number of individuals that share this haplotype and each hatch mark represents a mutation. Each haplotype is coloured according to the region they were sampled.\u003c/p\u003e","description":"","filename":"Fig.2Networkperregion.png","url":"https://assets-eu.researchsquare.com/files/rs-5955537/v1/57d80ff2add4c98936078e07.png"},{"id":76454459,"identity":"9b9177f4-c8de-4313-a48b-bedb27681dbe","added_by":"auto","created_at":"2025-02-17 10:22:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1160983,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of \u003cem\u003eGlyphis garricki\u003c/em\u003e kin pairs identified by Feutry et al. (2020) as an indicator of contemporary connectivity in Van Diemen Gulf (A, Adelaide River; S, Sampan Creek; W, Wildman River; WA, West Alligator River; SA, South Alligator River; EA, East Alligator River). The line width represents the number of kin pairs distributed within and between sampling locations. HSP, half-sibling pair; FSP, full-sibling pair; POP, parent-offspring-pair.\u003c/p\u003e","description":"","filename":"Fig.3Kinship.png","url":"https://assets-eu.researchsquare.com/files/rs-5955537/v1/295c10460cf2694251180e44.png"},{"id":82641104,"identity":"bfc594b5-dfcb-4896-9709-605737a74531","added_by":"auto","created_at":"2025-05-13 15:19:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4498857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5955537/v1/9b4cea78-885b-4296-827d-e71a7eaef7e3.pdf"},{"id":76454457,"identity":"3cd3f32c-b2fc-4997-b44d-54c98ca2a1b4","added_by":"auto","created_at":"2025-02-17 10:22:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5828382,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary material: R markdown containing all analyses performed for the present study.\u003c/p\u003e","description":"","filename":"DevlooDelvaetal.Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5955537/v1/0d97051cec806309c99f778c.pdf"},{"id":76454458,"identity":"1107bc9a-ab4a-49cc-a24a-d6bae31c4afe","added_by":"auto","created_at":"2025-02-17 10:22:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17024,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5955537/v1/bd6dab0612711367509c3815.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"Sex-specific dispersal patterns of the threatened Northern River Shark","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInformation on population structure and genetic, or reproductive, connectivity is crucial to assist the conservation and management of threatened species. Small, isolated populations may be susceptible to local extinction, whereas well-connected populations can maintain sufficient genetic variability and fitness (Caughley 1994; Frankham et al. 2017; Ralls et al. 2020). Nonetheless,\u0026nbsp;biases in sex ratios and/or dispersal rates among sexes can have implications for the sustainability of seemingly well-connected populations.\u0026nbsp;(Caughley 1994). Recently-established small populations may have lower arrivals from one sex and could be negatively impacted through elevated levels of inbreeding\u0026nbsp;(Bonte et al. 2012). In addition, the intensity, duration, and variety of threats can differ greatly between the dispersing and non-dispersing (i.e. resident or philopatric) sex\u0026nbsp;(Lucas et al. 1994).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhilopatry and sex-biased dispersal are important traits for conservation and resource management that both describe the individual-level dispersal behaviour of a species within and among populations, resulting in population-scale dynamics (Dobson 2013; Greenwood 1980). We define philopatry as the seasonal return migration of reproducing individuals to their natal site (natal philopatry) or region (regional philopatry), and sex-biased dispersal as the asymmetric reproductive movement rates of females over males (or vice versa) between distinct populations (Chapman et al. 2015; Phillips et al. 2021). Dispersal is typically undertaken by both sexes, but dispersal rate and distance can differ between females and males (Pusey 1987). For instance, Blundell et al. (2002) illustrated that male River Otters (\u003cem\u003eLontra canadensis\u003c/em\u003e) dispersed more frequently than females, but only to nearby areas, whereas females travelled greater distances. In elasmobranchs (sharks and rays), female philopatry and male-biased dispersal have been recorded, to date, in 62 and 25 species, respectively (Chapman et al. 2015; Flowers et al. 2016; Phillips et al. 2021).\u003c/p\u003e\n\u003cp\u003eMany methods are used to infer philopatry and sex-biased dispersal (Goudet et al. 2002; Prugnolle and de Meeus 2002). By following the matrilineal connectivity with mitochondrial DNA (mtDNA), studies have shown whether females historically revisited the same breeding or pupping grounds (i.e. female philopatry). The comparison of mtDNA with biparentally- or paternally-inherited nuclear DNA (nuDNA) allows the \u0026lsquo;indirect\u0026rsquo; inference of historical sex-biased gene flow when either nuDNA or mtDNA shows a different pattern of structuring between populations. However, secondary contact of two previously separated populations can lead to a similar mito-nuclear discordance (Toews and Brelsford 2012). A novel method to measure contemporary \u0026lsquo;reproductive\u0026rsquo; dispersal from juveniles\u0026nbsp;using genomic data\u0026nbsp;was proposed by\u0026nbsp;Bravington et al. (2016). This reproductive dispersal can be measured \u0026lsquo;directly\u0026rsquo; or \u0026lsquo;indirectly\u0026rsquo; by analysing the spatial distribution of parent-offspring pairs (POPs) or half-sibling pairs (HSPs), respectively. The latter was applied by\u0026nbsp;Feutry et al. (2017)\u0026nbsp;and\u0026nbsp;Patterson et al. (2022)\u0026nbsp;to study the Speartooth Shark (\u003cem\u003eGlyphis glyphis\u003c/em\u003e); a euryhaline species known to occur in a limited number of macrotidal tropical rivers of northern Australia and southern Papua New Guinea\u0026nbsp;(PNG; Pillans et al. 2009; White et al. 2015). They identified 121 cross-cohort (i.e. born in different years) HSPs using single nucleotide polymorphism (SNP) markers. Data from the full mitochondrial genome (mitogenome) indicated that all 18 cross-cohort, cross-river (i.e. sampled from different rivers) HSPs were likely paternally-related (i.e. different mtDNA haplotype), indicating that their fathers moved between populations (i.e. contemporary male-biased dispersal).\u003c/p\u003e\n\u003cp\u003eThe Northern River Shark (\u003cem\u003eGlyphis garricki\u003c/em\u003e) is a recently described euryhaline shark known from a small number of rivers and estuaries of northern Australia and southern PNG (Compagno et al. 2008; Feutry et al. 2020; Kyne et al. 2021b). It has prolonged use of estuarine environments within its life history, including their use for pupping and nursery areas, yet the location of mating aggregations are currently unknown (Grant et al. 2023; Pillans et al. 2009). The main threats to this species include fishing (target and bycatch) and habitat modification (Kyne et al. 2021c). It is globally listed as Vulnerable\u003cem\u003e\u0026nbsp;\u003c/em\u003eon the IUCN Red List of Threatened Species (Kyne et al. 2021c), and listed as Endangered on Australia\u0026rsquo;s Environment and Biodiversity Protection Act (EPBC Act 1999).\u003cem\u003e\u0026nbsp;\u003c/em\u003eFeutry et al. (2020) identified five distinct genetic populations across its distribution using 1,700 SNP markers: King Sound and Cambridge Gulf in Western Australia, Daly River and Van Diemen Gulf (VDG) in the Northern Territory, and southern PNG. The study showed that the King Sound population exhibited low genetic diversity. It also found both low historical and contemporary genetic connectivity among the populations and sampling locations (within populations) based on the number of migrants per generation (\u003cem\u003eNm\u003c/em\u003e) and kinship distribution. Whether there is a sex bias in dispersal among populations is currently unknown.\u003c/p\u003e\n\u003cp\u003eIn this study, we investigate the maternal evolutionary history and assess the possibility of female philopatry and male-biased dispersal in \u003cem\u003eG. garricki\u003c/em\u003e with three different types of data that capture population dynamics at different timescales. First, we evaluate the mitochondrial genetic variation among regions to detect historical female philopatry and demographic events. Second, we compare full mitogenome data, which have a higher resolution than conventional regions (e.g. control region; Feutry et al. 2014), against the SNP data described by Feutry et al. (2020) to infer any historical demographic differences between females and males, putatively driven by unequal gene flow or difference in effective population size. Third, we use mtDNA data to infer the paternal or maternal relationship between half siblings to assess contemporary philopatry and sex-biased dispersal at a fine spatial scale.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eSample collection and DNA extraction\u003c/h2\u003e\n\u003cp\u003eBetween 2012 and 2016, a total of 379 \u003cem\u003eG. garricki\u003c/em\u003e tissue samples were collected from 11 rivers, creeks, large marine embayments, or estuaries (hereafter referred to as sampling locations) in five different regions covering the entire known geographical range of the species (Fig. 1; Feutry et al. 2020; Kyne et al. 2021b; White et al. 2015). The same tissue samples as Feutry et al. (2020) were used in this study, with the exception of six PNG samples that were collected by Grant et al. (2021). Each shark was measured, sexed, and sampled for genetic material before it was released at the site of capture. The total length (TL) of all sharks ranged from 52 to 182 cm; most sharks were juveniles or sub-adults, with only 20 males \u0026gt;141 cm TL (sexually mature i.e. possessing calcified claspers; Feutry et al. 2020). Sexual maturity in female sharks cannot always be assessed externally, but seven females \u0026gt;153 cm TL were assumed to be mature based on the established male size-at-maturity (Feutry et al. 2020; Pillans et al. 2009). Genomic DNA was extracted following the standard protocol of the DNeasy Blood and Tissue kit (Qiagen Inc., Valencia, California, USA).\u003c/p\u003e\n\u003ch2\u003eMitogenome amplification and sequencing\u003c/h2\u003e\n\u003cp\u003eThe full mitochondrial genome was amplified with two primer pairs (A and B fragments; Supplementary material section 1.2), for all but eight samples that did not amplify. For these samples, primers that target quarter fragments of the mitogenome were designed (A1, A2, B1, and B2 fragments; Supplementary material section 1.2). Polymerase chain reactions (PCR) were performed in 30\u0026nbsp;\u0026mu;L reactions, following the standard proofreading Takara LA Taq protocol (Takara, Otsu, Shiga, Japan). PCR conditions were set to 1 min at 94\u0026deg;C for initial denaturation; then 40 cycles of denaturation (94\u0026deg;C, 30 s), annealing (55\u0026deg;C, 30 s), and extension (68\u0026deg;C, 10 min); concluding with a 10 min extension at 72\u0026deg;C. PCR products were cleaned following the Agencourt AMPure XP magnetic bead protocol (Beckman Coulter Inc., Indianapolis, Indiana, USA). Amplicons were quantified with a NanoDrop 8000 Spectrophotometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA) and the purified A and B fragments were pooled at equimolar concentration. Subsequently, these amplicons were simultaneously fragmented and barcoded with the Nextera XT DNA Sample Preparation kits and 96 sample Nextera Index kits (Illumina, San Diego, California, USA). The libraries were quantified with the Qubit dsDNA BR assay kit (Life Technologies, Carlsbad, California, USA) and normalized. Libraries were then pooled and sequenced on a Miseq desktop sequencer using the 2x250 bp paired-end reads MiSeq reagent kit v2 (Illumina, San Diego, California, USA).\u003c/p\u003e\n\u003ch2\u003eMitogenome assembly and alignment\u003c/h2\u003e\n\u003cp\u003eDemultiplexed fastq files were imported into Geneious prime software v2021.2.2 (Biomatters Ltd., Auckland, New Zealand), and the reads were paired. The Nextera adapters were trimmed and the reads were quality trimmed at a phred score \u0026lt;20 for a Kmer of 20 using the BBDuk tool as implemented in Geneious. Reads shorter than 50 bp after trimming were discarded from subsequent analyses. Reads for each individual were then mapped onto a previously published reference sequence (Feutry et al. 2015) using the \u0026lsquo;Map to Reference\u0026rsquo; tool in Geneious with the \u0026lsquo;high sensitivity\u0026rsquo; parameters and 10 iterations. The majority rule consensus (\u0026gt;50 % of mapped reads for any single mutation, insertion, or deletion) for each shark was exported.\u003c/p\u003e\n\u003cp\u003eIn addition to the 379 samples, we obtained another six \u003cem\u003eG. garricki\u003c/em\u003e mitogenomes from the Alligator rivers from NCBI Genbank (accession numbers: KF646786, KT698042, KT698044, KT698053, KT698059, NC_023361; Feutry et al. 2015; Li et al. 2015). All mitogenome sequences were aligned with the \u0026lsquo;multiple align\u0026rsquo; tool and the MUSCLE algorithm (Edgar 2004).\u003c/p\u003e\n\u003ch2\u003eGenetic variation and haplotype analysis\u003c/h2\u003e\n\u003cp\u003eThe mitogenome alignment and raw nuclear SNP data from Feutry et al. (2020) were imported into R 4.4.0 (R Core Team 2024) using the \u003cem\u003eapex\u0026nbsp;\u003c/em\u003ev1.0.6 and \u003cem\u003edartRverse\u003c/em\u003e v1.0.2 packages (Gruber et al. 2018; Jombart et al. 2017; Mijangos et al. 2022). Both mtDNA and nuclear SNP datasets were filtered so that they contained the same individuals, except the PNG mtDNA data that contained six different samples (Supplementary material). Nucleotide diversity (\u0026pi;), theta based on segregating site (\u0026theta;), haplotype diversity (h), and haplotype networks were calculated with the \u003cem\u003epegas\u003c/em\u003e v1.3 package\u0026nbsp;(Paradis 2010). Mitochondrial diversity estimates were compared against the nuclear DNA results from\u0026nbsp;Feutry et al. (2020).\u003c/p\u003e\n\u003ch2\u003eGenetic differentiation and historical demography\u003c/h2\u003e\n\u003cp\u003eA global Analysis of Molecular Variance (AMOVA) was performed with \u003cem\u003epegas\u0026nbsp;\u003c/em\u003e(10,000 permutations) to detect population differentiation among the five regions and 11 sampling locations within regions. Fixation indices (\u0026Phi;\u003csub\u003eST\u003c/sub\u003e) were calculated with 10,000 permutations between sampling locations and between regions with the \u0026lsquo;popStructTest\u0026rsquo; function in the \u003cem\u003estrataG\u003c/em\u003e v2.5.0.1 package (Archer et al. 2017). We further compared the mitochondrial\u0026nbsp;\u0026Phi;\u003csub\u003eST\u003c/sub\u003e values against the nuclear F\u003csub\u003eST\u003c/sub\u003e and kinship results from\u0026nbsp;Feutry et al. (2020).\u003c/p\u003e\n\u003ch2\u003eKinship analyses\u003c/h2\u003e\n\u003cp\u003eKin relationships in VDG were identified by Feutry et al. (2020) based on 379 individuals. We re-analysed the half sibling pairs in light of their maternal mtDNA relationship, year of birth, and spatial distribution within and between six\u0026nbsp;sampling locations\u0026nbsp;to infer sex-specific connectivity at a contemporary timescale\u0026nbsp;(see Patterson et al. 2022). Adult dispersal can also be directly observed (i.e. by sampling the adult that moved) from parent-offspring pairs that were distributed between sampling locations, where we assumed that the oldest individual (i.e. parent) dispersed. The occurrence of cross-river full-sibling pairs (FSPs) was used to examine the incidence of juvenile dispersal. This is expected to be minimal based on the close association to nursery habitat, limited linear extent of river occupancy and the salinity preference of juvenile euryhaline sharks\u0026nbsp;(Grant et al. 2023; Pillans et al. 2009).\u003c/p\u003e\n\u003cp\u003eSex-specific adult dispersal was inferred with an indirect approach (i.e. without sampling adults) by comparing the mtDNA haplotypes of HSPs. First, cross-cohort (i.e. born in different years), cross-river HSPs inform if parents moved between locations between breeding seasons. By comparing the mtDNA haplotypes of each pair (h1=h2 or h1\u0026ne;h2), we can infer which parent, the mother or the father, is more likely to have moved between sampling locations. Sex-specific dispersal rates were calculated by comparing number of mothers or fathers that moved between rivers versus the ones that returned to the same river between breeding seasons. Alternatively, cross-cohort, same-river HSPs reveal how likely mothers and fathers are to return to the same river between breeding seasons (i.e. natal philopatry). Philopatry rates were calculated by comparing number of mothers or fathers that returned to the same river versus the ones that moved between rivers between breeding seasons. Additionally, the null hypothesis of \u0026lsquo;no maternal/paternal philopatry\u0026rsquo; was statistically tested with an approximate likelihood ratio (\u0026Delta;) test and randomising same and cross-river HSPs over 10,000 permutations, developed by Feutry et al. (2017). This test took the mtDNA haplotype frequencies into account to calculate the likelihood that\u0026nbsp;maternally/paternally-related HSPs are more likely to occur in the same river. This likelihood approach is important when several haplotypes are common in the population. Each estimate of sex-biased dispersal, female philopatry and male philopatry was given a range of uncertainty by assuming that a HSP with the same haplotype might be paternally related if the frequency of the haplotype was higher than 50% in the river of collection.\u003c/p\u003e\n\u003cp\u003eLastly, since sample size influences the number of kin pairs found (Bravington et al. 2016), we corrected the number of HSPs by the number of pairwise comparisons performed (HSPcorr). For each sampling locations in VDG, the ratio of \u0026lsquo;HSPcorr within\u0026rsquo; over the sum of \u0026lsquo;HSPcorr between\u0026rsquo; (HSPcorr\u003csub\u003ewithin\u003c/sub\u003e/\u0026sum;HSPcorr\u003csub\u003ebetween\u003c/sub\u003e) was calculated to provide a non-gender specific estimate of philopatric and dispersive behaviours. Specifically, a ratio of \u0026gt;1 indicated that more individuals bred with individuals from the same location than they did with individuals from other locations (i.e. stronger philopatric behaviour), and conversely a ratio of \u0026lt;1 implied higher connectivity.\u003c/p\u003e\n\u003ch2\u003eLength-at-age function\u003c/h2\u003e\n\u003cp\u003eThe connectivity inference based on the spatial distribution of kin relied on the fact that we could assign each individual to the year it was born with a reasonable amount of certainty. No age-and-growth studies have been performed on \u003cem\u003eG. garricki\u003c/em\u003e. There are limited opportunities to obtain such data as \u003cem\u003eG. garricki\u003c/em\u003e is a protected species in Australia and ageing of elasmobranchs is commonly estimated by examining growth bands in large sample sizes of vertebrae. In order to assign the age cohorts, Bravington et al. (2019) fitted a von Bertalanffy growth function to 34 recaptured sharks with a maximum size of 155 cm TL and a maximum recapture interval of two years:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"206\" height=\"27\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u003cem\u003eL\u003csub\u003et\u003c/sub\u003e\u003c/em\u003e is the TL at capture (in cm), \u003cem\u003eL\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e is the length at birth (50.0 cm), \u003cem\u003eL\u003c/em\u003e\u003cem\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/em\u003e is asymptotic length (154.7 cm), \u003cem\u003eK\u003c/em\u003e is the growth coefficient (0.139 year\u003csup\u003e-1\u003c/sup\u003e), and \u003cem\u003et\u003c/em\u003e is the age (in years). While limitations of the growth function are acknowledged (e.g. \u003cem\u003eL\u003c/em\u003e\u003cem\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/em\u003e is likely underestimated, leading to an overestimation of \u003cem\u003eK\u003c/em\u003e), age cohort assignment for size classes closer to \u003cem\u003eL\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e (i.e. juveniles) will be less prone to error as these age cohorts will capture a wider length range. However, larger individuals approaching \u003cem\u003eL\u003c/em\u003e\u003cem\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/em\u003e will have an exponentially increasing likelihood of falling in their own age cohort. Because the majority of specimens used were small (juveniles or subadults \u0026lt;141 cm TL), sufficient assignment of individuals to age cohorts was achieved for the purpose of this study. Specimens of \u0026gt;154.7 cm TL (i.e. \u003cem\u003eL\u003c/em\u003e\u003cem\u003e\u003csub\u003e\u0026infin;\u003c/sub\u003e\u003c/em\u003e) could not be assigned to age cohorts and were excluded from this part of the analysis. We used the full sibling pairs (FSP) from the same river and caught in the same time period (2 weeks apart) to calculate a fixed standard deviation of length-at-age (0.43 cm) to the mean\u0026nbsp;\u003cem\u003eL\u003csub\u003et\u003c/sub\u003e\u003c/em\u003e of each cohort\u0026nbsp;(Supplementary material section 10.2.2).\u0026nbsp;In addition, an extra 0.5 year was added to the upper and lower ranges of \u003cem\u003et\u003c/em\u003e for each age cohort to account for instances where catch dates were not aligned with the austral summer pupping season (e.g. cohort 1 \u003cem\u003et\u003c/em\u003e = 0\u0026ndash;0.5, cohort 2 \u003cem\u003et\u003c/em\u003e = 0.5\u0026ndash;1.5 etc). This conservative approach aimed to capture the variable range of lengths that may occur in each age cohort resulting from varied individual growth rates and birth sizes.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eMitogenome assembly and alignment\u003c/h2\u003e\n\u003cp\u003eThe full mitogenomes of 379 samples were sequenced with an average of 98,360 reads sequenced per sample. Two samples from the South Alligator River (VDG) had low coverage (\u0026lt;1,000 mapped reads) and were omitted from further analyses. Overall, the mitogenome length was 16,702\u0026ndash;16,703 bp and consisted of 26 polymorphic sites across the remaining 383 sharks (including the six mitogenome sequences from NCBI). These included two insertions in tRNA-Tyr (-/T, n = 3) and Control Region (-/A, n = 28), and one deletion in tRNA-Cyt (G/-, n = 1). One region between tRNA-Pro and the Control Region consistently had low coverage (\u0026lt;100 mapped reads), most likely indicating the presence of a secondary structure. This may explain why eight samples did not amplify well and had to be amplified in quarter sections (Supplementary material section 1.2). All new \u003cem\u003eG. garricki\u003c/em\u003e sequences were uploaded to NCBI GenBank (accession numbers: MW652871\u0026ndash;MW653247).\u003c/p\u003e\n\u003ch2\u003eGenetic variation and haplotype analysis\u003c/h2\u003e\n\u003cp\u003eThe average\u0026nbsp;\u0026pi;\u0026nbsp;for the 383 aligned sequences was 0.000095 (variance,\u0026nbsp;\u0026sigma;\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.000001). Most variable sites had low diversity (\u0026pi;\u0026nbsp;= 0.02\u0026ndash;0.1), but three sites had\u0026nbsp;\u0026pi;\u0026nbsp;= 0.25\u0026ndash;0.5 (Supplementary material section 4.1). The 383 sequences resulted in 26 haplotypes with a haplotype diversity of 0.7670 (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e = 0.000233). The diversity indices per region and sampling location are summarised in Table 1 and Supplementary material sections 4.2 and 4.3, respectively. Here, we see that Cambridge Gulf, specifically West Cambridge Gulf, had the highest\u0026nbsp;\u0026pi;\u0026nbsp;and King Sound had the lowest. Van Diemen Gulf had the highest haplotype diversity (h = 0.659). This pattern becomes most obvious when illustrated in a haplotype network (Fig. 2; Supplementary material section 5). Eight haplotypes were singletons. All but two haplotypes were private to a single geographical region (Supplementary material section 4.3), and the haplotypes from PNG and two haplotypes from Cambridge Gulf were most distant (4\u0026ndash;5 mutations). Other than these haplotypes, the network mainly shows a signal of expansion with one central haplotype (HT25) and the other haplotypes spreading out by one mutation at a time.\u003c/p\u003e\n\u003ch2\u003eGenetic differentiation and historical demography\u003c/h2\u003e\n\u003cp\u003eOverall, the AMOVA showed that the differentiation among regions and sampling locations within regions was highly significant (\u0026Phi;\u003csub\u003eST\u003c/sub\u003e = 0.710; p \u0026lt; 0.0001; Supplementary material section 6.1). Specifically, most high pairwise\u0026nbsp;\u0026Phi;\u003csub\u003eST\u003c/sub\u003e values (0.77\u0026ndash;0.94) were observed between PNG and all other sampling locations, except for West Cambridge Gulf (Table 2). The lowest\u0026nbsp;\u0026Phi;\u003csub\u003eST\u003c/sub\u003e was observed between close sampling locations, such as within VDG (e.g. the Alligator rivers). Interestingly, some sampling locations within VDG (East Alligator River and Sampan Creek) appeared less differentiated from King Sound than geographically closer regions (Daly River and Cambridge Gulf).\u003c/p\u003e\n\u003ch2\u003eKinship analyses\u003c/h2\u003e\n\u003cp\u003eIn Van Diemen Gulf, Feutry et al. (2020) identified 4 parent-offspring pairs (POPs), 34 full-sibling pairs (FSPs), and 130 half-sibling pairs (HSPs) from 108 811 pairwise comparisons (Table 3 and Table 4). These pairs could be merged into 73 family groups (208 unique individuals); 43 groups consisted of only single pairs (POP, FSP or HSP), but 30 contained multiple pairs per group (i.e. a combination of POPs, FSPs, and HSPs; Supplementary material section 10.1). The mitogenome of eight samples could not be amplified or had a low sequencing coverage, which resulted in 12 HSPs with missing haplotype information. All father-offspring pairs had different haplotypes and all FSPs had a matching haplotype. Twenty-six and 101 HSPs had non-matching and matching haplotypes, respectively (Table 3; Supplementary material section 10.3).\u003c/p\u003e\n\u003cp\u003eOverall, 54 same-cohort and 77 cross-cohort HSPs were identified. Eight same-river HSPs and two cross-river HSPs could not be assigned to a cohort due to missing length data or a size that was too large (i.e. \u0026gt;154.7 cm TL) for the growth function. All POPs were assigned to different cohorts and all but one FSPs were assigned to the same cohort, thus indicating that the growth function can assign individuals to an approximate cohort. One POP was distributed between the Wildman and South Alligator Rivers; the other three POPs were found between the East Alligator and South Alligator Rivers (Fig. 3). All FSPs were juveniles or sub-adults (\u0026lt;141 cm TL) and all, except one, were found within the same river.\u003c/p\u003e\n\u003cp\u003eSixteen cross-cohort, cross-river HSPs were also identified, of which 10 had different haplotypes and six had matching haplotypes (Table 3). However, of the latter six HSPs, three had very common haplotypes (HT06, HT13, and HT25; frequencies \u0026gt; 50 %; Table 3). For example, of the 16 cross-cohort, cross-river HSPs, four were distributed between the Adelaide River and the other rivers in VDG. Three out of the four pairs exhibited different haplotypes and were most likely paternally-related (Fig. 3), which would suggest that the father dispersed. Further, 56 cross-cohort HSPs shared a haplotype; six of these were caught in different rivers. Of the 56 HSPs with the same haplotype, 30 pairs (including three cross-river) shared a haplotype that was common in the sampling location (\u0026gt; 50 %; Table 3). A formal likelihood ratio test showed that HSPs with the same haplotype are more likely to be found in the same river than across river (\u0026Delta;\u0026nbsp;= 14.195, p \u0026lt; 0.0001), suggesting contemporary female philopatric behaviour. Conversely, male philopatry was not supported (\u0026Delta;\u0026nbsp;= 0.008, p = 1.000; Supplementary material section 10.3.3).\u003c/p\u003e\n\u003cp\u003eWhen correcting the number of HSPs for the number of pairwise comparisons performed (Supplementary material section 10.4), we found that two sampling locations in VDG (Adelaide and Wildman Rivers) showed a stronger philopatric signal (\u0026gt;1). In contrast, we saw that Sampan Creek and the East Alligator River shared more kin between sampling locations, than retained within (\u0026gt;1). The West Alligator and South Alligator Rivers had an approximately equal ratio of same-river and cross-river kin (~1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study applied three different types of data to assess fine-scale population demography at three different timescales. The results provide new information on historical maternal population structure, as well as contemporary sex-specific dispersal of \u003cem\u003eG. garricki\u003c/em\u003e, using the close-kin results from Feutry et al. (2020), supplemented with new whole mitogenome data. Specifically, the mitogenome results provide evidence of historical colonisation events and range expansion, as well as secondary contact between two separated lineages. Based on the 177 kin pairs, we observed that 63 % (10 out of 16) of the cross-cohort, cross-river half-sibling pairs (HSPs) are paternally-related and 89 % (50 out of 56) of the same-river, cross-cohort HSPs are maternally-related, indicating high male dispersal rates and female philopatry respectively. Mitochondrial results also show that each sampling location has significant differentiation. Combined with high nuclear SNP fixation indices (Feutry et al. 2020), this suggests strong historical population structure at a very fine spatial scale.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMaternal demographic history\u003c/h2\u003e\n\u003cp\u003eClear mitochondrial differentiation between all sampling location was detected in this study and is consistent with the lack of connectivity reported by Feutry et al. (2020). However, low\u0026nbsp;\u0026Phi;\u003csub\u003eST\u003c/sub\u003e between the Ord and Daly rivers is most likely explained by ongoing gene flow or the retention of ancestral haplotypes in recently diverged populations\u0026nbsp;(incomplete lineage sorting; Toews and Brelsford 2012). This concurs with results from\u0026nbsp;Feutry et al. (2020)\u0026nbsp;that \u003cem\u003eG. garricki\u003c/em\u003e started its range expansion recently in the Gulf of Carpentaria (Fig. 1) and subsequently expanded both westwards and north-eastwards. In addition, the mitogenome analyses revealed that PNG was most different from all sampling locations. This would suggest that PNG and northern Australian sharks may have been separated for a long period of time with limited female gene flow. Results also show two haplotypes in the Cambridge Gulf that are more similar to PNG, indicating either secondary contact between sharks from Cambridge Gulf and PNG, or an unsampled (or extinct) population. This is substantiated by a similar mitogenome observation for the congeneric \u003cem\u003eG. glyphis\u003c/em\u003e (Kyne et al. 2021a)\u0026nbsp;and the clustering of \u003cem\u003eG. garricki\u003c/em\u003e samples between PNG and Cambridge Gulf based on the nuclear SNP data\u0026nbsp;(Feutry et al. 2020).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFemale philopatry and male-biased dispersal\u003c/h2\u003e\n\u003cp\u003eOn an evolutionary timescale, we observe high pairwise\u0026nbsp;\u0026Phi;\u003csub\u003eST\u003c/sub\u003e between most sampling locations. This suggests that females consistently return to the same river for breeding, which is most likely the site they were born (i.e. natal philopatry). Additionally, some disjuncture between mitochondrial and nuclear data is apparent. The mitogenome differentiation from King Sound to other\u0026nbsp;regions\u0026nbsp;is less pronounced than in the nuclear data from Feutry et al. (2020), with the lowest differentiation between King Sound and the East Alligator River. Such \u0026lsquo;mito-nuclear discordance\u0026rsquo; is likely driven by range expansion towards King Sound with incomplete mtDNA lineage sorting (Toews and Brelsford 2012) and an accumulation of nuDNA mutations at the edge of a range expansion due to the effect of drift on a small and recently founded population (Feutry et al. 2020; Peischl et al. 2013).This is again supported by the low genetic diversity in King Sound as suggested by Thorburn and Morgan (2004). These demographic events will affect the non-recombining haploid mtDNA, and recombining diploid nuDNA, differently (Lawson Handley and Perrin 2007; Phillips et al. 2021) Nonetheless, the low mitochondrial diversity towards the edge of the range expansion (i.e. King Sound) indicates a low influx of genetically diverse females and/or a disproportional amount of male colonisation. Overall, the high and significant mitochondrial and nuclear fixation indices between most sampling locations would suggest that both males and females show historical philopatric behaviours.\u003c/p\u003e\n\u003cp\u003eOn a contemporary timescale based on kinship in Van Diemen Gulf, we see evidence that females are more likely to return to their river of birth for parturition (i.e. natal philopatry). This trans-generational female philopatry is inferred from the result that 50 out of 56 of the cross-cohort HSP sharing a mother (i.e. same mtDNA haplotype) were found in the same river. However, 30 of these 56 HSPs (including three cross-river) shared a haplotype that was common in the sampling location (frequencies \u0026gt; 50 %) and could represent paternally-related HSPs. Thus, the female bias in philopatric behaviour ranged between 79.3 % (23/29) and 94.3 % (50/53). Further, we observe a bias towards male dispersal, evidenced by the four father-offspring pairs across rivers and 10 out of 16 cross-river, cross-cohort HSPs that were paternally related (i.e. different haplotype). If we consider the 30 cross-cohort HSPs that share a haplotype with high frequency (potentially paternally-related), the amount of male dispersal ranged between 23.2 % (10/43) and 68.4 % (13/19). Previously, Feutry et al. (2017) and Patterson et al. (2022) demonstrated that male \u003cem\u003eG. glyphis\u003c/em\u003e are more likely to disperse between the Adelaide River and Alligator rivers. In the current study, we showed a similar contemporary male-biased dispersal pattern for \u003cem\u003eG. garricki\u003c/em\u003e between the Adelaide River and the other VDG rivers (75 % male bias). Yet, between more closely located VDG rivers, the sex bias was less pronounced. Overall, this information on female philopatry and male-biased dispersal adds to the growing theory of elasmobranch dispersal (Chapman et al. 2015; Flowers et al. 2016; Phillips et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConnectivity over three different timescales\u003c/h2\u003e\n\u003cp\u003eIn general, we see that the three different approaches (mtDNA, nuDNA, and kinship) reinforce each other. When pairwise fixation indices are high, few kin are found between sampling locations and vice versa. For example, kinship data suggest stronger philopatric behaviour in the Adelaide and Wildman Rivers, demonstrated by the higher fixation indices and the many kin pairs retained within these sampling locations. We also found high reproductive connectivity (i.e. low fixation indices and many cross-river HSPs) between spatially close rivers in VDG, such as Sampan Creek and the East Alligator River. The increased connectivity is not explained solely by geographic proximity (Supplementary material section 10.4). Low mating opportunities, food availability, or environmental fluctuations may be responsible for the relatively few same-river kin pairs in these sampling locations (Comins et al. 1980; Greenwood 1980; Hamilton and May 1977).\u003c/p\u003e\n\u003cp\u003eOn a few occasions we observed a discordance between marker types. Non-significant\u0026nbsp;\u0026Phi;\u003csub\u003eST\u003c/sub\u003e, but significant F\u003csub\u003eST\u003c/sub\u003e, were found between three pairwise comparisons. As mentioned before, this likely reflects the retention of ancestral polymorphisms in the mitochondrial genome, yet the statistical power to detect population structure of thousands of SNPs is also expected to be higher than a single mtDNA marker (Morin et al. 2009). Another conflict between methods was demonstrated by the many cross-river kin between the Alligator rivers as opposed to high and significant pairwise fixation indices. This could reflect that these adjacent sampling locations have only recently been connected. Overall, we show that the close-kin method, supplemented with mtDNA, is a valuable tool for defining fine-scale population structure, provided that sampling is spatially and temporally extensive with sufficient covariate data (Bravington et al. 2016; Patterson et al. 2022).\u003c/p\u003e\n\u003ch2\u003eConservation implications\u003c/h2\u003e\n\u003cp\u003eAs a euryhaline species, \u003cem\u003eGlyphis garricki\u003c/em\u003e is susceptible to population decline due to exposure to both riverine and marine pressures (Grant et al. 2019; Pillans et al. 2009). Fishing, particularly commercial gillnetting, likely poses the largest threat to \u003cem\u003eG. garricki\u0026nbsp;\u003c/em\u003ein Australia (DCCEEW 2023; Kyne and Feutry 2017), while small-scale fisheries pose a severe threat in its PNG range (Amepou et al. 2024). This species is also inherently susceptible to habitat modification (such as changes to natural freshwater flow regimes) and degradation of riverine and coastal environments (Grant et al. 2019). Since \u003cem\u003eG. garricki\u003c/em\u003e is ecologically confined to estuaries in early life stages (Grant et al. 2023; Pillans et al. 2009), its ability to migrate from unfavourable conditions to neighbouring estuaries via marine coastal waters may be restricted. The present results support this ecological confinement and susceptibility, and further highlight gene flow implications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar to Feutry et al. (2020), we see that the King Sound population has the lowest diversity and the lowest \u0026theta;. This supports previous statements that any environmental or anthropogenic changes in this area will likely affect \u003cem\u003eG. garricki\u003c/em\u003e (Morgan et al. 2011; Thorburn and Morgan 2004). Further, we confirmed that each\u0026nbsp;sampling location\u0026nbsp;within VDG forms a unique genetic unit, with high female philopatry and male-biased dispersal. This means that for isolated populations the immigration of males cannot compensate for the removal of local females, and if a river/estuary were to be impacted by increased levels of mortality, natural recovery would not be guaranteed. As such, protection at the smallest spatial scale is essential to ensure local viability of populations, while allowing male dispersal to maintain genetic connectivity. Collectively, given the relatively small population size estimates of \u003cem\u003eG. garricki\u0026nbsp;\u003c/em\u003e(Bravington et al. 2019), threats and their associated mortality rates need to be mitigated, as they may have disproportionate impacts on each sex.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the most comprehensive overview available on the spatial population structure of the threatened shark \u003cem\u003eG. garricki\u003c/em\u003e. We investigated connectivity on both evolutionary and contemporary timescales. It provides novel insights into contemporary sex-specific dispersal using a close-kin framework, and shows that full mitogenomes can add a new dimension to the kinship analyses, as well as resolve important historical events, such as the connectivity between Cambridge Gulf and PNG. In addition, we found that females exhibit a strong philopatric behaviour and that mainly males disperse within Van Diemen Gulf. This study indicates that each sampling location should be managed as a separate unit, since gene flow is not uniform and females appear to return to the same river. Lastly, this study highlights the importance of a multi-method approach to provide crucial information for conservation and management of threatened species and their environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Marine Biodiversity Hub, a collaborative partnership\u003cem\u003e\u0026nbsp;\u003c/em\u003esupported through funding from the Australian Government\u0026apos;s National Environmental Science\u003cem\u003e\u0026nbsp;\u003c/em\u003eProgram. This research was funded in part through an Ord River Research Offset grant through CSIRO. Floriaan Devloo-Delva was supported by a joint UTAS/CSIRO scholarship and the Quantitative Marine Science program. We thank the many Traditional Owners, assistants, and volunteers that assisted with field work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe note no conflict or competing interests among the authors in relation to the information provided in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData archiving\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMitochondrial genomes are uploaded to NCBI GenBank (accession numbers: MW652871 - MW653247) and alignment, metadata and Rmarkdown are available from the CSIRO Data Access Portal: https://doi.org/10.25919/hpf0-d336 (Devloo-Delva et al. 2025). SNP genotypes and metadata from Feutry et al. (2020) are available at https://doi.org/10.5061/dryad.hqbzkh1ch\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSharks were sampled under Northern Territory Fisheries Special Permits S17/3252 and S17/3364, Kakadu National Park Research Permit RK805, Western Australian Department of Fisheries Exemption No. 2630, Western Australian Department of Parks and Wildlife Permit SF010485, and Charles Darwin University Animal Ethics Committee Approval A11041. Samples from Papua New Guinea (PNG) were obtained opportunistically through observations of small-scale fisheries (Grant et al. 2021; White et al. 2015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was designed by FD, PMK, and PF. Funding was secured by PMK, RDP, and TS. Samples were provided by PMK, MIG, GJJ, DLM, RDP, and WTW. Lab work was performed by FD, JRM, RMG, and PMG. The data was analysed by FD and PF. The manuscript was drafted by FD and all authors contributed to revising the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmepou Y, Chin A, Foale S, Sant G, Smailes O, Grant MI (2024). Maw money, maw problems: A lucrative fish maw fishery in Papua New Guinea highlights a global conservation issue driven by Chinese cultural demand. Conservation Letters\u003cstrong\u003e:\u003c/strong\u003e e13006.\u003c/li\u003e\n\u003cli\u003eArcher FI, Adams PE, Schneiders BB (2017). strataG: An R package for manipulating, summarizing and analysing population genetic data. Mol Ecol Resour 17(1)\u003cstrong\u003e:\u003c/strong\u003e 5-11.\u003c/li\u003e\n\u003cli\u003eBlundell GM, Ben-David M, Groves P, Bowyer RT, Geffen E (2002). 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Rediscovery of the Threatened River Sharks, \u003cem\u003eGlyphis garricki \u003c/em\u003eand \u003cem\u003eG. glyphis\u003c/em\u003e, in Papua New Guinea. PLoS One 10\u003cstrong\u003e:\u003c/strong\u003e e0140075.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Genetic variation in the mitogenome (16,703 bp) of \u003cem\u003eGlyphis garricki\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion/\u003cem\u003esampling location\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026pi;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026theta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u0026theta;\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKing Sound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCambridge Gulf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e2.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;West Cambridge Gulf\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e2.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;Ord River\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDaly River\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVan Diemen Gulf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;Adelaide River\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;Sampan Creek\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;Wildman River\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;West Alligator River\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;South Alligator River\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;East Alligator River\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3964%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePapua New Guinea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.46847%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8559%;\"\u003e\n \u003cp\u003e0.000040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84685%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: n, number of samples; \u0026pi;, nucleotide diversity; H, haplotype richness; h, haplotype diversity;\u0026nbsp;\u0026theta;, theta (where \u0026theta;\u0026nbsp;= 2N\u003csub\u003eef\u003c/sub\u003e\u0026micro;); and \u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u0026theta;\u003c/sub\u003e, variance of theta.\u003c/p\u003e\n\u003cp\u003eTable 2: Pairwise fixation indices per sampling location for \u003cem\u003eGlyphis garricki\u003c/em\u003e. Below diagonal: mitochondrial DNA\u0026nbsp;\u0026Phi;\u003csub\u003eST\u003c/sub\u003e (this study). Above diagonal: nuclear DNA F\u003csub\u003eST\u003c/sub\u003e (Feutry et al. 2020). Non-significant results after Bonferroni correction (p \u0026gt; 0.0009) are underlined. Sampling locations were King Sound (KS), Cambridge Gulf (WC, West Cambridge Gulf; O, Ord River), Daly River, Van Diemen Gulf (A, Adelaide River; S, Sampan Creek; W, Wildman River; WA, West Alligator River; SA, South Alligator River; EA, East Alligator River), and Papua New Guinea (PNG).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"824\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Phi;\u003csub\u003eST\u003c/sub\u003e\\ F\u003csub\u003eST\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePNG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKing Sound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.297\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.302\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.290\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.287\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.274\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.270\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.271\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.259\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.268\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.395\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWest Cambridge Gulf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.337\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.093\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.120\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.121\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.123\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.156\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOrd River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.768\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.243\u003csup\u003e*\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.096\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.128\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.128\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.126\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.126\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.129\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.128\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.154\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDaly River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.671\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.313\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.060\u003csup\u003e*\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.091\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.089\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.090\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.180\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdelaide River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.473\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.469\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.728\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.702\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.174\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSampan Creek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.178\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.370\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.543\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.561\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.233\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.170\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWildman River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.392\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.494\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.653\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.655\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.491\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.081\u003csup\u003e*\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.171\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWest Alligator River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.750\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.638\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.871\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.822\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.715\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.294\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.127\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.173\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Alligator River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.326\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.526\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.609\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.618\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.249\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.036\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.140\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.221\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.172\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEast Alligator River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.126\u003csup\u003e**\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.425\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.545\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.559\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.222\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cu\u003e0.005\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.180\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.365\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.082\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.172\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePapua New Guinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.901\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.574\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.931\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.893\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.862\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.767\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.828\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.938\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.818\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.798\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p \u0026lt; 0.0001; ** p \u0026lt; 0.005; * p \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003eTable 3: \u003cem\u003eGlyphis garricki\u003c/em\u003e kin pairs identified in Van Diemen Gulf by Feutry et al. (2020). The haplotype data (same \u0026lsquo;=HT\u0026rsquo;, different \u0026lsquo;\u0026ne;HT\u0026rsquo;, or \u0026lsquo;missing) from this study allows the inference of philopatric behaviour and sex-specific connectivity. The number in parentheses indicates the half-sibling pairs with a matching haplotype, where the haplotype has a high frequency (\u0026lt;50 %) and could be paternally related. These numbers were used to estimate the minimum and maximum ranges of philopatry and dispersal rates.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFather-offspring pairs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull sibling pairs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHalf sibling pairs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e=HT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026ne;HT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e=HT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026ne;HT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e=HT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026ne;HT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSame-river, same-cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e35(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eLitter size and multiple paternity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSame-river, cross-cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e50(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePhilopatry*\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; Female: 50/56 [23/29\u0026ndash;50/53]\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; Male: 6/16 [6/19\u0026ndash;33/43]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSame-river, missing-cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e4(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCross-river, same-cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e4(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eDirect movement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCross-river, cross-cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e6(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSex-specific connectivity*\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; Male: 10/16 [10/43\u0026ndash;13/19]\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; Female: 6/56 [3/56\u0026ndash;6/29]\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCross-river, missing-cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;*This study focusses on philopatry and sex-specific connectivity.\u003c/p\u003e\n\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glyphis garricki, close-kin, SNP, mitogenome, philopatry, sex-biased dispersal","lastPublishedDoi":"10.21203/rs.3.rs-5955537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5955537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Quantifying genetic connectivity between populations of a species is important to understand its conservation and management needs. Genetic connectivity implies gene flow among discrete populations occurring via the dispersal of individuals outside their population of origin, followed by reproduction. This process can be biased between sexes, with increasing evidence of sex-biased dispersal in elasmobranchs (sharks and rays). In this study we assessed the historical and contemporary connectivity of the threatened Northern River Shark (\u003ci\u003eGlyphis garricki\u003c/i\u003e) using mitochondrial genomes, complemented with a genealogical framework based on close-kin relationships. Almost all of the 11 sampling locations in five regions across their known range formed a distinct breeding unit, with those locations in close geographical proximity sharing more kin. Close-kin results, based on cross-river half siblings and mitochondrial haplotypes, suggested that males had higher dispersal rates (63 % [23–68 %]), compared to females (10 % [5–20 %]). High fixation indices (global ΦST = 0.71) and a large proportion of same-river maternal half siblings (89 % [79–95 %]) indicated that females returned to their river of origin for pupping (i.e. natal philopatry on historical and contemporary timescales). By including full mitochondrial genome data, close-kin methods can detect sex-biased contemporary connectivity over the last couple generations. This is crucially important for the framing of conservation and management actions, and could require a sex-specific assessment of threats.","manuscriptTitle":"Sex-specific dispersal patterns of the threatened Northern River Shark","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 10:22:07","doi":"10.21203/rs.3.rs-5955537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51f994d9-3d20-4419-ae38-3d76ca761954","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44269858,"name":"Biological sciences/Genetics/Population genetics"},{"id":44269859,"name":"Biological sciences/Ecology/Molecular ecology"},{"id":44269860,"name":"Biological sciences/Evolution/Population genetics"}],"tags":[],"updatedAt":"2025-05-13T15:11:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-17 10:22:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5955537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5955537","identity":"rs-5955537","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}