Conservation genetics of Mahogany Gliders and insights into their evolutionary relationship with Squirrel Gliders

Conservation genetics of Mahogany Gliders and insights into their evolutionary relationship with Squirrel Gliders | 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 Research Article Conservation genetics of Mahogany Gliders and insights into their evolutionary relationship with Squirrel Gliders Yiyin Chang, Lorenzo V. Bertola, Kyall R. Zenger, Conrad J. Hoskin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5041688/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2025 Read the published version in Conservation Genetics → Version 1 posted 9 You are reading this latest preprint version Abstract Squirrel Gliders (Petaurus norfolcensis) are widely distributed throughout the woodlands of eastern Australia, while the similar but larger Mahogany Glider (Petaurus gracilis) inhabits the coastal woodlands of the Wet Tropics in northeastern Queensland. The Mahogany Glider is an Endangered species due to habitat loss and fragmentation. To inform effective conservation management, this study used single nucleotide polymorphism markers (SNPs) from field and museum-derived samples to investigate genetic relationships within the Squirrel/Mahogany Glider complex and conduct a conservation genetics assessment for the Mahogany Glider. Analyses of genetic structure, phylogenomics, and outlier loci identified four genetic groups: Mahogany Glider and three distinct groups in Squirrel Gliders (North Queensland, Cape Cleveland, and mid-eastern/south-eastern Queensland). We found genetic admixture between these groups, but whether the admixture is historic or current remains unclear. Gliders from North Queensland were genetically more similar to Mahogany Gliders in some analyses than to the other Squirrel Glider groups. Morphological analysis confirmed that Mahogany Gliders are distinguishable from other gliders by their larger body size and longer tail. The study emphasizes the taxonomic uncertainty in the Mahogany-Squirrel Glider complex and the need to investigate glider populations at contact zones. When assessing Mahogany Gliders alone, we found a clear north-south split in genetic structuring, with the southern cluster being more structured than the northern cluster. Genetic diversity within Mahogany Gliders was generally comparable to that of Squirrel Gliders, but some sampling localities indicated loss of genetic diversity and low effective population size. Regardless of whether Mahogany Gliders are classified as a species or subspecies, their Endangered status underscores the need for targeted conservation efforts. The genetic findings offer practical pathways for on-ground management to enhance population recovery and connectivity. Habitat Fragmentation Petaurus gliders Threatened Species Genomics SNPs Genetic Structure Genetic Diversity Taxonomic Uncertainty Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Habitat destruction poses a significant threat to wildlife species, with estimates suggesting this threat impacts nearly 90% of threatened species and is the primary driver of extinction (Hogue and Breon 2022). Loss of habitat often leads to fragmentation, hence transforming once-interconnected habitats into smaller, isolated patches. In these fragmented landscapes, wildlife populations decline and become isolated, suffering from harmful edge effects, habitat degradation, and a loss of connectivity (Bender et al. 1998; Laurance et al. 2007; Didham 2010). Small, isolated populations are more vulnerable to genetic stochasticity, which can lead to the loss of genetic diversity and inbreeding depression (Willi et al. 2007; Frankham et al. 2017; Lino et al. 2019). Ultimately, loss of genetic diversity diminishes a species’ adaptive potential to environmental changes (Hedrick 2000; Charlesworth and Willis 2009) and can lead to extinction (Frankham 2005). Integrating genetic tools into conservation efforts enhances our understanding of habitat loss and fragmentation impacts, facilitating targeted management strategies (Luikart et al. 2003; Kohn et al. 2006). One powerful tool is the analysis of Single Nucleotide Polymorphisms (SNPs), which uses thousands of specific genome positions with nucleotide substitutions to provide high-resolution information on individual differences, population structure, and genetic diversity (Marth et al. 1999; Schork et al. 2000; Primmer 2009; Steiner et al. 2013). The results can then be used to target conservation efforts, such as identifying areas for revegetation to improve population connectivity (e.g., Bertola et al. 2023) and detecting local adaptation to environment change (e.g., McCulloch et al. 2021; Giska et al. 2022). Importantly, genomic tools can also reveal cryptic species (Dufresnes et al. 2019) and help resolve taxonomic uncertainty, which is crucial because conservation efforts are focussed on formally designated taxa (Dufresnes et al. 2019, 2023). The Squirrel Glider ( Petaurus norfolcensis ) and the Mahogany Glider ( Petaurus gracilis ) are both medium-sized gliding possums inhabiting eucalyptus open forests in eastern Australia. Squirrel Gliders are distributed from Victoria to North Queensland, while Mahogany Gliders are restricted to a 120 km stretch of wet sclerophyll lowland forests in North Queensland between Ingham and Tully (Van Dyck et al. 2013). The distributions of the two species are not known to overlap (Van Dyck 1993; Jackson and Claridge 1999; Goldingay and Jackson 2004; Sharpe and Goldingay 2010). Initially considered a subspecies of the Squirrel Glider (Iredale and Troughton 1934), the Mahogany Glider was reclassified as a distinct species due to morphological differences (Van Dyck 1993). The Mahogany Glider is larger, with a body length of 215–265 mm and weight of 255–500 g, compared to the Squirrel Glider’s 180–244 mm length and 173–300 g weight. It also has a longer, less fluffy tail (300–390 mm vs. 220–300 mm) (Van Dyck 1993; Jackson 2011; Jackson and Schouten 2012). In skull morphology, the Mahogany Glider has a narrower interorbital width but larger skull length, rostral height, and zygomatic width (Van Dyck 1993; Jackson 2011). Previous studies have observed morphological and body size variation in Squirrel Gliders but data from gliders in eastern and northern Queensland is limited (Stobo-Wilson et al. 2020). The genetic differentiation between Mahogany and Squirrel Gliders also remains unresolved. Earlier research using two mitochondrial genes (ND2 and ND4) and two nuclear markers (ω-globin and ApoB gene) showed low sequence divergence (1.8–2.2%) between the species, and the phylogeny displayed a single admixed clade (Malekian et al. 2010; Ferraro 2012). No detailed genomic assessment of genetic differentiation and relatedness between the two species has been conducted to date, thereby creating taxonomic uncertainty that hinders accurate species identification and effective conservation strategies. The Mahogany Glider is listed as Endangered under the Environment Protection and Biodiversity Conservation (EPBC) Act of 1999. Within its small distribution, the glider relies on mature lowland forests with large, diverse trees that provide essential tree hollows and year-round flower nectar (Jackson 2000). Agricultural deforestation, particularly for sugarcane and cattle farming, has reduced its habitat by approximately 40% (Jackson et al. 2011; Jackson et al. 2019). Furthermore, suitable habitat is now fragmented and includes more than 400 habitat patches smaller than 1 km 2 (Chang et al. 2022). Habitat loss and severe fragmentation can reduce and structure genetic diversity and may have resulted in poor genetic consequences in some isolated, small populations(Frankham et al. 2010). Understanding genetic diversity across populations of the Mahogany Glider is a key priority in the recovery plan (Parson and Latch 2006) and other conservation assessments (Curtis 2012; Burbidge et al. 2014). Despite the recognition of the importance of population genetic assessments to efficiently manage threatened species (e.g., Frankham et al. 2017), none has been conducted for the Mahogany Glider. Here, we present the first detailed population genetic assessment of the Mahogany Glider, and we place this within the broader context of Squirrel Glider populations in Queensland. Our aims were to: (1) investigate the evolutionary relationship and key morphological characteristics between Mahogany and Squirrel Gliders, and (2) assess the population genetic structure and genetic diversity of the threatened Mahogany Glider. The results provide a foundation for resolving the taxonomy of Mahogany and Squirrel Gliders in Queensland and inform the conservation management of Mahogany Gliders. Methods Fieldwork — surveys and sample collection A total of 16 trapping surveys were conducted to collect tissue samples from Mahogany Gliders ( Petaurus gracilis ) and Squirrel Gliders ( P. norfolcensis ) at 14 distinct sites between 15 th April 2021 and 31 st August 2022, for a total of 1,525 trap nights (Fig. 1; Table S1). We also collected tissue samples from Krefft’s Gliders ( P. notatus ), a smaller glider species that is sympatric with Mahogany and Squirrel Gliders. At each site, we strapped 20 wire cage traps (56 x 20 x 20 cm) to trees 2.5–4 m above the ground and 100–400 m apart depending on habitat size and suitability. The back half of each cage trap was covered with waterproof plastic sheeting for rain shelter. Each trap contained a bait ball made of peanut butter, honey and oats to attract gliders to the traps (Jackson 2001; Knipler et al. 2021). We squeezed additional honey on top of the bait ball to increase bait smell and keep the bait moist. We also sprayed a solution of water, raspberry cordial and honey above the trap as a scent lure. Traps were baited and opened just before sunset (5 pm) and checked at 11 pm and each morning before sunrise (5 am). Captured individuals were weighed, sexed, and measured (head length and width, body length, and tail length). We collected a tissue biopsy sample from the edge of an ear using a small ear punch and preserved the sample in 90% ethanol. We took photos of the face from the front and the side, and the whole body next to a scale bar. The glider was then released at the point of capture. All equipment was sterilized using 70% ethanol after each capture. A total of 44 Mahogany Gliders, 6 Squirrel Gliders and 9 Krefft’s Gliders samples were trapped and sampled in the field surveys (Table S1). Five of the 16 surveys conducted yielded no captures. For the sites with catching success, trapping rates were generally low, ranging from 0.8% to 15%. The highest trapping rates were observed in the northern section of Paluma Range National Park, particularly at Bambaroo, Easter Creek, and Allendale (Fig. 1; Table S1). Additional samples were obtained from five rescued Mahogany Gliders (via wildlife-carer Daryl Dickson, MGDD01−05, Table S2) and from a previous trapping survey during 2008 and 2010 conducted by Queensland Parks and Wildlife Service (via Mark Parsons, MGMP01−08, Table S2). We also included historical samples of 10 Mahogany Glider and 43 Squirrel Glider from the Queensland Museum (Table S2). The Squirrel Glider samples extended from southeastern to far north Queensland (Fig. 1). The museum samples, collected between 1989 and 2017, were sourced from fur, skin, liver, or muscle specimens that were obtained from field-specimens, rescued individuals, or deceased animals. SNP genotyping and filtering A total of 125 tissue samples were genotyped: 67 from Mahogany Gliders, 49 from Squirrel Gliders, and 9 from Krefft’s Gliders (Fig. S1; Table S2). DNA extraction and SNP genotyping were performed at Diversity Arrays Technology (DArTseq) in Canberra, using the DArTseq method with PstI and Sphlv4 restriction enzymes. Genomic DNA was extracted using the Macherey-Nagel NucleoMag Plant kit and subjected to high-density sequencing (2.5 million reads per individual) on an Illumina NovaSeq 6000 S2 flow cell, referencing the Petaurus DArTseq (1.0) genomic library (Jaccoud et al. 2001; Kilian et al. 2012). Single Nucleotide Polymorphism (SNP) calling was performed using the DArTsoft14 algorithm within the KDCompute pipeline developed by Diversity Arrays Technology (http://www.kddart.org/kdcompute.html). We performed SNP quality control using a customized R script (Fig. S1; ESM1) and dartR v2.7.2 (Gruber et al. 2018) to ensure standardized sequence quality (R v4.2.2; R Development Core Team 2022; Rstudio team 2023). Individuals with more than 35% missing genotypes (i.e., a low call rate) and single nucleotide polymorphisms (SNPs) with more than 10% missingness were removed. To ensure reliable and consistent genotyping results, SNPs with extreme read depth (50) and reproducibility (consistency of SNPs calling result) lower than 0.99 were also removed. Secondary SNPs (i.e., SNPs called from the same locus) were removed by retaining the SNP with the higher reproducibility. We further applied minor allele count, linkage disequilibrium, and outlier loci filters to both the complete dataset and the species-specific datasets. Briefly, singleton SNPs (minor allele count equals to one) were removed (O’Leary et al. 2018). The loci under linkage disequilibrium were filtered out with a threshold of 0.9 using PLINK v1.90b6.26 (Purcell et al. 2007) and bigsnpr v1.11.6 (Purcell et al. 2007; Prive et al. 2018), retaining only one of the linked markers with higher call rate. To identify loci under selection, we conducted outlier analysis using three distinct methods: OutFLANK v0.2 (Whitlock and Lotterhos 2015), Bayescan v2.1 (Foll and Gaggiotti 2008), and pcadapt v4.3.3 (Luu et al. 2017). The results from OutFlank and Bayescan did not reveal any outlier loci. Using pcadapt, we discovered the outlier loci that significantly contribute to the genetic structure. We performed principal component analyses with a false discovery rate of 0.01. Outlier loci were then filtered based on their significance using two thresholds: the highly conservative Bonferroni method and the moderately conservative Benjamini-Hochberg method (Luu et al. 2017). To maximize the information retained, we created two distinct datasets based on the outlier filter: (1) a dataset containing only neutral loci, with outlier loci removed using the Bonferroni method, for genetic structure analysis; (2) a separate dataset comprising solely outlier loci, identified through the Benjamini-Hochberg method, for signatures of selection analysis. First-degree relatives and potentially duplicated samples were identified using the KING method of moments in SNPRelate v1.32.0 (Manichaikul et al. 2010; Zheng et al. 2012). For first-degree relatives, only the individual with the highest call rate was retained for genetic structure and diversity analyses. Downstream analyses were conducted in two steps. First, to better understand the evolutionary relationship between Mahogany and Squirrel Gliders, population genetic structure was assessed with a dataset including all three species (Fig. S1). For each of the identified genetic clusters, we then evaluated genetic distance, genetic diversity, morphological characteristics, and signatures of selection. Second, we conducted a conservation genetic assessment focusing on the Mahogany Glider. Different datasets were used based on the assumptions and information required for each analysis (Funk et al. 2012) (Table 1). Phylogenetic analyses were performed on all loci, including both neutral and outlier loci. Genetic structure and diversity analyses were conducted on neutral loci only, while signature of selection analysis was investigated with outlier loci only. Therefore, the SNPs number varied across datasets. An overview of the analysis workflow is presented in Table 1, and the detailed steps to produce the different datasets are summarised in Fig. S1. Table 1 Overview of the genetic analyses presented in this study, highlighting the dataset used. The table details the dataset used for each type of analysis for (i) all gliders, (ii) Mahogany and Squirrel Gliders together (MG-SQ), (iii) Mahogany Gliders only (MG), and (iv) Squirrel Gliders only (SQ). Analysis Species/Loci subset All gliders MG-SQ MG SQ DAPC, STRUCTURE Neutral Loci Neutral Loci Neutral Loci NetView Neutral Loci AMOVA All Loci Neutral Loci Outlier Loci Neutral Loci Outlier Loci Mantel test (Isolation by distance) Neutral Loci Neutral Loci Genetic differentiation (F ST ) Neutral Loci Neutral Loci Genetic diversity Neutral Loci Neutral Loci Neutral Loci Neutral Loci Phylogenetic Tree All Loci Signature of Selection Outlier Loci Outlier Loci 1. Genetic and morphological assessment of Mahogany and Squirrel Gliders Interspecific population structure To identify genetically distinct populations, we analysed genetic structure with a dataset of neutral loci for all three Petaurus species sampled (N = 86). NetView uses the k-nearest neighbours (kNN) approach to visualize genetic distance matrices (Neuditschko et al. 2012). These matrices were computed using three distinct methods: Euclidean distance applied on allele frequency within individuals (eucl) (Jombart and Ahmed 2011), pairwise difference on number of loci for which individuals differ (nLoci) (Paradis and Schliep 2019), and number of allelic differences between two individuals (nAllele) (Kamvar et al. 2014). We used the Discriminant Analysis of Principal Components (DAPC) and the unsupervised membership grouping in adegenet v2.1.8 to visualize genetic clustering patterns without assumptions about sampling localities (Jombart and Collins 2015). DAPC reduces the dimensionality of genetic data to identify the underlying population structure (Jombart et al. 2010). The unsupervised membership grouping on the sampling localities were compared and visualized using K-means clustering. In response to recent critiques of PCA in genetic analyses (REF), we reported the variance explained by the first two PCs and used additional methods to confirm the population structure. To investigate genetic structure and admixture jointly, we used the Bayesian clustering method of STRUCTURE v2.3.4 (Pritchard et al. 2000; Falush et al. 2003; Falush et al. 2007; Hubisz et al. 2009). Ten replicates for each K value ranging from 1 to 10 were performed and the results were extracted using pophelper v2.3.1 (Francis 2017). The optimal K value was determined by identifying the peak of ∆K in the Evanno plots (Evanno et al. 2005). Isolation by distance for each of Mahogany and Squirrel Gliders was investigated by testing correlations between geographical Euclidean distance and pairwise individual genetic distance (proportion of alleles shared) using Mantel tests (Gruber et al. 2018). The results of Mantel tests were then visualized using MASS v.7.3 (Kemp 2002). Genetic differentiation (F ST ) analyses were performed to assess the extent of variation explained between the four genetic groups based on neutral loci. These analyses were performed using the bootstrapped method (hierfstat v0.5, Goudet 2005) and Analysis of Molecular Variance (AMOVA) (poppr v2.9.3, Kamvar et al. 2014). Phylogenomics To clarify the phylogenetic relationships between Mahogany and Squirrel Gliders, we constructed a maximum likelihood phylogenetic tree using IQ-TREE v2.2.2.2 (Minh et al. 2020). The tree was built using both neutral and outlier loci with monomorphic loci and missing data removed (N = 6,501) (Fig. S1). We used ModelFinder Plus in IQ-TREE to identify the substitution model (Kalyaanamoorthy et al. 2017). A maximum likelihood tree was then computed using the selected substitution model (TVM+F+I+G4), in conjunction with the ultrafast bootstrap method with 30,000 replicates. The resulting phylogenetic tree was visualized using iTOL (Letunic and Bork 2021), with Krefft’s Gliders rooted as the outgroup based on available literature (Malekian et al. 2010; Cremona et al. 2020) and the population structure analyses of this study. Analysis of signatures of selection among consensus genetic groups Individual loci that significantly deviate from average genome-wide population divergence patterns may indicate the presence of selection. Therefore, we assessed potential local adaptation of the identified genetic groups using the outlier loci dataset derived from the SNP genotyping and filtering section above. We used DAPC in R package adegenet v2.1.8 to assess clustering patterns of individuals based on the outlier loci (Jombart and Collins 2015). Morphological assessment of consensus genetic groups Once the consensus genetic groups were identified across the above analyses, we assessed morphological differences among them. We measured body length (snout-vent length) and tail length on live individuals captured in the field and on specimens housed in the Queensland Museum (Brisbane). The museum specimens included both wet (spirit) and dry (skin) specimens. Additionally, head length and head width were taken when specimens contained skulls. We conducted a Factor Analysis of Mixed Data (FAMD) for the total of 118 Mahogany Glider and 79 Squirrel Gliders. The FAMD analysis integrated both categorical (specimen type, sex, tail character) and continuous data (body, tail, head length and width) into a principal component analysis (Kassambara 2016). To address missing values, the regression method from R package missMDA v1.19 was applied (Husson and Josse 2023). The analysis was conducted using the FactoMineR v2.9 (Lê et al. 2008) and factoextra v1.07 (Kassambara and Mundt 2020). 2. Conservation genetic analyses of Mahogany Gliders The genetic analyses in this section were based on the Mahogany Glider-only data. Genetic diversity was assessed based on neutral loci and the data was filtered to remove markers monomorphic for Mahogany Gliders. Population genetic structure was assessed following the methodology described in the interspecific population structure section above. Additionally, the effective population size for each sampling locality was estimated using the linkage disequilibrium method (and assuming a monogamous mating system) in NeEstimator v2.1 (Jackson 2000b; Do et al. 2014). Only Mahogany Glider samples collected between 2017 and 2022 were used, to prevent overlapping generations. To evaluate the conservation genetics of Mahogany Gliders, we compared genetic diversity of Mahogany Gliders to that of the consensus genetic groups in Squirrel Gliders. We quantified individual genetic diversity using observed and expected heterozygosity (H o /H e ) and standardized multi-locus heterozygosity (sMLH), as per the methodology in dartR v2.9.7 (Gruber et al. 2018) and inbreedR v0.3.3 (Stoffel et al. 2016), respectively. The genetic diversity of each sampling locality, consensus genetic group, and species was assessed using several indices, including averaged sMLH, H o /H e , Wright’s inbreeding index F IS (Gruber et al. 2018), and allelic richness (Ar) corrected using the rarefaction method (Adamack and Gruber 2014). To ensure the accurate estimation of heterozygosity, any loci with missing data were excluded (Schmidt et al. 2021). Additionally, we included monomorphic loci to examine their effect on the estimation of genetic diversity (Schmidt et al. 2021). Results SNP genotyping and filtering DArTSeq genotyping identified 67,261 single nucleotide polymorphisms (SNPs) across 115 individuals from all three species (Fig. S1). For nine samples, DNA extraction was unsuccessful. The quality control process, which considered call rate, reproducibility, secondary loci, and read depth, filtered out low-quality loci and removed an additional 18 low-quality samples (16 from museums and 2 from old field collections). Most of the failed museum samples were fur samples (Fig. S2). After these steps, 97 individuals remained with 10,408 SNPs. Filtering based on minor allele count and linkage disequilibrium excluded 3,455 loci from the all-species dataset, 948 loci from the Mahogany Glider dataset, 1,598 loci from the Squirrel Glider dataset, and 2,514 loci from the Krefft’s Glider dataset. Outlier analysis identified 349, 37, and 32 loci as outliers in the all-species dataset, Mahogany Glider dataset, and Squirrel Glider dataset, respectively (Table 1). No outliers were identified in the Krefft’s Glider dataset because of the low sample size of nine individuals. Kinship analyses revealed the presence of three pairs of duplicates (kinship coefficient > 0.354, (Manichaikul et al. 2010) indicating three individuals were sampled in the field twice. Additionally, six pairs of first-degree relatives (kinship coefficient > 0.16) were identified among the Mahogany Glider samples in Bambaroo and Easter Creek, as well as one triplet of first-degree relatives among Squirrel Glider samples from near Airlie Beach. Among the first-degree relatives, the individual with the highest call rate was retained for genetic structure and diversity analyses. The number of SNPs in each of the analysis datasets was: 9,258 for all-species dataset (N = 97), 9,651 for Mahogany-Squirrel Glider dataset (N = 88), 9,719 for Mahogany Glider dataset (N = 58), and 8,941 for Squirrel Glider dataset (N = 30). 1. Genetic and morphological assessment ofMahogany and Squirrel Gliders Interspecific population structure In all analyses, Krefft’s Gliders from the Wet Tropics formed a highly distinct group compared to Mahogany and Squirrel Gliders. In the NetView analyses, Krefft’s Gliders were not joined to any other species, even when the nearest neighbours (kNN) parameter was set to 30 (Fig. 2A; Fig. S3). In the DAPC analyses, Krefft’s Gliders were identified as a highly distinct group that is well-distinguished from the other two species with a high eigenvalue (7608 with all loci, 2927 with neutral loci) (Fig. 2B). In the STRUCTURE analyses, Krefft’s Glider also emerged as genetically distinct, showing no evidence of genetic admixture with other species (Fig. 2C). Results of AMOVA also showed that the outlier loci (N = 542) explained 70% of variation when Krefft’s gliders were included (Fig. S7). Therefore, below we present results based on neutral loci of Mahogany and Squirrel Glider only. The genetic structure of Mahogany Gliders exhibits some degree of differentiation from Squirrel Gliders, although the extent of this differentiation varies across different analyses. NetView analyses utilizing Euclidean distance matrices (eucl) consistently delineated Mahogany and Squirrel Gliders into two distinct groups (Fig. S3), maintaining separation even up to a kNN value of 42. Conversely, the other two distance matrices (nLoci and nAllele) demonstrated a convergence between Squirrel and Mahogany Gliders at kNN values of 15 and 25, respectively. DAPC distinguished Mahogany and Squirrel Gliders with a high eigenvalue of 1890 (Fig. 2B), although only 37% of total variance was explained. STRUCTURE analyses also suggested an optimal clustering at K = 2, demarcating the Mahogany Glider from the Squirrel Glider (Fig. 2C; Fig. S5). Three distinct groups of Squirrel Gliders were identified: (1) gliders from the mid-eastern and south-eastern regions of Queensland, extending from Brisbane to Charters Towers (SQ), (2) gliders from Cape Cleveland (CC), and (3) gliders from Chillagoe, Tolga, and Princess Hills north of Townsville (NQ) (Fig. 2). The SQ group exhibited considerable variation and consistently formed its own cluster, distinct from the Mahogany Gliders (Fig. 2). The assignment of the CC group varied across analyses. In most structure analyses, these gliders were grouped with the Squirrel Gliders (Fig. 2A; Fig. S4A), but in some analyses, they formed their own distinct cluster (Fig. 2B, C; Fig. S4A, B). The NQ samples, which comprise a broad distribution from Townsville to Chillagoe in north Queensland, were particularly interesting. Unlike the SQ group, these samples were grouped with Mahogany Gliders in most of the structure analyses, rather than with Squirrel Gliders (Fig. 2A, B; Fig. S4A). Despite the clear genetic distinction between Mahogany Gliders and Squirrel Gliders, evidence of introgression between the two species is evident. A gradient of admixture is observed in Squirrel Gliders from north of Mackay to the southern and northwestern range of the Mahogany Glider (Fig. 2C). In the STRUCTURE analysis (K = 2), minor introgression is detected between Mahogany Gliders (MG) and Squirrel Gliders north of Mackay, but more than half of the genetic composition of the CC and NQ groups originates from Mahogany Gliders (Fig. 2C). The NQ group, identified as Squirrel Gliders based on morphology and collection localities, were connected with Mahogany Gliders in the NetView analysis at kNN = 30 (Fig. 2A). This pattern persisted in the unsupervised membership grouping, where these samples consistently clustered with Mahogany Gliders (Fig. S4). Even in the STRUCTURE analysis (K = 2 and K = 5), the NQ samples predominantly displayed genetic components from Mahogany Gliders, with some admixture from CC (Fig. 2C). Phylogenomic analysis further support recognition of four genetic groups The maximum likelihood tree based on the all-species dataset using both neutral and outlier loci conforms with the population genetic results presented above (Fig. 3; Fig. S1). Krefft’s Gliders form a distinct and divergent group, while the relationships among Mahogany and Squirrel Gliders are complex. All Mahogany Glider samples cluster into a single clade, yet this clade is nested within the broader Squirrel Glider clade. Within this broader clade, the North Queensland (NQ) Squirrel Gliders are the most divergent group, forming a sister clade to the clade that includes the subclades of Mahogany Glider, Cape Cleveland Squirrel Gliders (CC), and mid-eastern/south-eastern Queensland Squirrel Gliders (SQ). These clades and subclades have high bootstrap support (98–100; ­­­­Fig. 3). Additionally, the Mahogany Glider clade is divided into two groups: a northern group (North MG) and a southern group (South MG), separated by the Cardwell Range. 2). Notably, the F ST estimates between Mahogany Gliders and the three Squirrel Glider groups (NQ, CC, and SQ) were lower (0.07–0.14) compared to the differentiation observed among the three Squirrel Glider groups themselves (0.17–0.19; Table 2) Table 2 Pairwise F ST calculated based on neutral loci (lower unshaded diagonal) between the four consensus genetic groups as determined by the results of population genetic structure and phylogenetic analyses. These groups are Mahogany Gliders (MG), the North Queensland individuals (NQ), the Cape Cleveland individuals (CC), and the remaining Squirrel Gliders (SQ). F ST / Group MG NQ CC SQ MG NA NQ 0.07 NA CC 0.14 0.19 NA SQ 0.14 0.19 0.17 NA Signatures of selection In the analysis of molecular variance (AMOVA) of the all-species dataset, the outlier loci explained around 65% of the variance between species, whereas only about 10% of the variance was attributed to the four consensus genetic groups (Fig. S7). However, in the AMOVA focusing only on Mahogany and Squirrel Gliders, the variance explained between species became negative. Instead, nearly 75% of the genetic variation was explained by the outlier loci within the four genetic groups (Fig. S7). The DAPC plot based on 101 outlier loci explained 82% of the total variance among the genetic groups (Fig. 4). PC1 (x-axis) had a high eigenvalue of 1908 and primarily differentiated CC Squirrel Gliders from Mahogany Glider and NQ Squirrel Gliders. PC2 (y-axis), with an eigenvalue of 691, further distinguished mid-eastern/south-eastern Queensland (SQ) Squirrel Gliders from the other three genetic groups. The plot also shows distinct genetic clustering of CC and SQ, while MG and NQ show significant overlap (Fig. 4). Morphological assessment among consensus genetic groups The Mahogany Glider was originally described as a larger glider, with a relatively longer and more slender tail, compared to Squirrel Gliders. The measurements in this study generally confirmed these morphological differences (Fig. 5A). MG individuals were larger, with longer bodies and longer tails compared to NQ, CC, and SQ (Fig. 5A). However, they are not distinct for relative tail length, with the mean tail-to-body length ratio for NQ and CC being similar to that of Mahogany Gliders (Fig. 5A). Compared to the other groups, SQ have relatively shorter tails. However, larger sample sizes are required for NQ and CC. The Factor Analysis of Mixed Data (FAMD) revealed that individuals identified as Mahogany versus Squirrel Gliders could be distinguished by a combination of body length, tail length, head length, head width, and tail base thickness (slender versus wide/fluffy tail base); however, there was some overlap (Fig. 5B). The first dimension of the FAMD accounted for 36.5% of the variation, with body length, head length, and tail length contributing 26.25%, 26.15%, and 24.65%, respectively. Nevertheless, it is important to interpret the results cautiously due to different specimen types and limited numbers of individuals for NQ and CC. 2. Conservation genetics of Mahogany Gliders After confirming that Mahogany Gliders are a moderately distinct genetic and phenotypic group through the analyses above, we conducted a genetic assessment of this Endangered taxon. Genetic structure analysis Two distinct genetic clusters were identified within Mahogany Gliders. These clusters correspond to the sampling localities north of the Herbert River/Cardwell Range (Muller’s Creek, Cardwell, Murray Upper, and Tully) versus the sampling localities south of the Herbert River (Ollera Creek, Bambaroo, Allendale, and Easter Creek) (Fig. 1; Fig. 6). In the Discriminant Analysis of Principal Components (DAPC), K-means clustering identified two clusters as optimal, explaining 44% of total variation. The northern and southern clusters were separated by the first eigenvalue (811.8), while the second eigenvalue (106) showed some discrimination among localities within each of the northern and southern clusters (Fig. 6A). The Evanno plots generated during the STRUCTURE analysis supported the identification of two clusters (K) as optimal, aligning with the clustering observed in the DAPC analysis (Fig. S5). However, F ST between the northern and southern clusters was relatively low (F ST = 0.054, 95% CI: 0.051–0.058), and the unsupervised membership grouping only identified one cluster within Mahogany Gliders. The northern cluster demonstrated greater genetic homogeneity, while the southern cluster showed more genetic substructure (Fig. 6B). Upon identifying the optimal two clusters (K = 2) in the STRUCTURE analysis, the southern cluster demonstrated genetic admixture from the northern cluster, though not reciprocally. At the second optimal clustering (K = 6), the northern cluster remained virtually homogenous, but the southern cluster showed more substructure. Interestingly, the Bambaroo site displayed its own genetic subcluster and had the least genetic admixture compared to other sampling localities in the southern cluster (Fig. 6B). NetView analyses yielded similar results — while all Mahogany Gliders form a distinct cluster when the nearest neighbours were set to 20 (kNN = 20), at kNN = 10, three distinct groups emerged: northern, southern, and Bambaroo groups (Fig. S3). The Mantel test results showed a statistically significant but weak isolation by distance, with only 12.6% (R 2 = 0.126) of the variation explained by geographical distance among the Mahogany Glider samples (Fig. S9A). Notably, after grouping the gliders into northern and southern clusters based on genetic structure analysis, genetic distances within the northern cluster exhibit a stronger correlation with geographical distance (R 2 = 0.236) (Fig. S9C). Comparative genetic diversity and effective population size estimates The neutral genetic diversity between Mahogany Gliders and Squirrel Gliders is similar. Mahogany Gliders exhibit a standardized multi-locus heterozygosity (sMLH) of 1.13, while Squirrel Gliders have a sMLH of 0.895. The observed heterozygosity (Ho) is 0.116 for Mahogany Gliders and 0.089 for Squirrel Glider, and the expected heterozygosity (He) is 0.13 for Mahogany Gliders and 0.118 for Squirrel Glider (Table S3). Note that the inbreeding coefficient (F IS ) is higher in Squirrel Gliders in comparison to Mahogany Gliders, likely due to the mixture of structured populations (Wahlund effect) (De Meeûs 2018). Within Mahogany Gliders (MG), the sMLH ranges from 0.78 to 1.06, with an average of 0.98. The H o varies from 0.14 to 0.19, averaging at 0.17, and the F IS ranged from 0.01 to 0.20, with an average of 0.07 (Table 3; Table S3). The northern cluster showed lower genetic diversity indices compared to the southern cluster, with individuals from Murray Upper—the northernmost site of their current known range—exhibiting the lowest genetic diversity (sMLH = 0.776, FIS = 0.201). Bambaroo, despite its small size and isolation, displayed a wide range of sMLH (0.75–1.26) and H o (0.13–0.22) values, with a low F IS of 0.05 (Fig. S8; Table 3). The effective population size could only be estimated for the three sampling localities with more than six samples: Allendale, Bambaroo, and Easter Creek (Table 3; Table S4). In Allendale, the effective population size was low, with a mean of 37 individuals (parametric CI: 35.4–38.8, Jackknife CI: 7.8–infinite). Bambaroo also exhibited a low effective population size, with a mean of 27.6 individuals (parametric CI: 27.1–28.1, Jackknife CI: 19–45.1). Conversely, Easter Creek displayed a notably high effective population size, with a mean of 431.2 individuals (parametric CI: 324.4–640.6, Jackknife CI: 45.8–infinite). Table 3 Genetic diversity metrics and effective population size estimates for Mahogany Gliders, including south and north populations, and sampling localities. Metrics provided are the number of individuals (nInd), standardized multilocus heterozygosity (sMLH), observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (FIS), and effective population size estimates (Ne). Refer to Table S3 and S4 for a full table with standard deviations and confidence intervals that includes Squirrel Glider genetic groups. Dataset Group nInd sMLH Ho He FIS Ne All-species MG 49 1.13 0.116 0.13 0.114 - Mahogany Glider South MG 29 1.04 0.184 0.204 0.111 - North MG 20 0.942 0.167 0.186 0.124 - Allendale 8 1.063 0.188 0.189 0.068 64.7 Bambaroo 11 1.028 0.182 0.184 0.054 27.6 Cardwell 8 0.973 0.172 0.177 0.085 - Easter Ck 7 1.044 0.185 0.186 0.077 431.2 Muller 5 1.039 0.184 0.175 0.053 - Murray Upper 4 0.776 0.138 0.151 0.201 - Ollera Ck 3 1.014 0.18 0.158 0.053 - Tully 3 0.921 0.163 0.149 0.085 - Discussion Aiming to inform effective conservation management of the Endangered Mahogany Glider, we utilised genomic tools at both broad and fine scales. We firstly examined the population genetic structure of the Mahogany Glider ( Petaurus gracilis) , Squirrel Glider ( P. norfolcensis ) and Krefft’s Glider ( P. notatus ) to gain insight on their evolutionary relationships and genetic distinctiveness of the Mahogany Glider. Subsequently, we conducted a conservation genetic assessment for the Mahogany Glider. The analyses confirmed that the Krefft’s Glider is highly divergent from the other two species. In contrast, genetic relationships between Mahogany and Squirrel Gliders are more complex. Genetic structure and phylogenetic analyses consistently identified four genetic groups: Mahogany Gliders from the lowlands of the Wet Tropics, ‘Squirrel Gliders’ from inland North Queensland (NQ), ‘Squirrel Gliders’ from Cape Cleveland near Townsville (CC), and Squirrel Gliders from mid-eastern and south-eastern Queensland (i.e., from about Charters Towers and Proserpine south). Mahogany Gliders are generally distinct from the other three genetic groups in the genetic analyses but with genetic admixture evident with the NQ and CC genetic groups (­­­Fig. 2). The genetic admixture between Mahogany and Squirrel Gliders suggests that historical or contemporary introgression has occurred between these species. The complexity of relationships between Mahogany and Squirrel Gliders has been previously suggested in phylogenetic studies on the Petaurid gliders. For instance, Malekian et al. (2010) revealed a genetic difference of only 1.8–2.2% between Mahogany and Squirrel Gliders for two mitochondrial genes (ND2 and ND4) and one nuclear marker (ω-globin). A different phylogenetic study, based on ND2 mitochondrial gene and ApoB1 nuclear gene, clustered Mahogany Glider samples with Squirrel Glider samples from Hervey Range (west of Townsville) and Einasleigh Uplands (west of Atherton Tablelands) (Ferraro 2012). The phylogenomic relationship we present here shows Mahogany Gliders nested within the broad Squirrel Glider clade but does show Mahogany Gliders as a highly supported, distinct clade (­­­­Fig. 3). We found substantial introgression between Mahogany Gliders and the gliders from North Queensland (NQ) (samples collected near Princess Hills, Atherton Tablelands, and Chillagoe) (Fig. 2C). The NQ gliders also group closely to Mahogany Gliders in signature of selection based on outlier loci and structure analyses based on neutral loci (e.g., NetView, Fig. 2A; DAPC, Fig. 2B; Signature of selection, Fig. 4). The low F ST value between NQ gliders and Mahogany Gliders further support the close relationship between them (Table 2). Interestingly, in the SNPs-based phylogeny (Fig. 3­­­­), the NQ samples are divergent to a monophyletic group of Mahogany and Squirrel Gliders, rather than being clustered within it (­­­­Fig. 3). The morphology is interesting for the NQ gliders — they are of similar body size to Squirrel Gliders, but their relative tail length is more akin to Mahogany Gliders (albeit based on a small sample size) (Fig. 5). Overall, the results suggest a close genetic relationship between NQ gliders and Mahogany Gliders, and whether hybridisation or cryptic (sub)species exist in NQ gliders requires further sampling and analyses (Malinsky et al. 2018). The gliders from Cape Cleveland (CC) are identified as a genetically distinct group in most of the analyses. They are more closely related to Squirrel Gliders but show signs of introgression from Mahogany Gliders (e.g., NetView, Fig. 2A; Structure, Fig. 2C; phylogeny, ­­­­Fig. 3). In some analyses, they appear as a distinct group (e.g., DAPC, Fig. 2B; Pairwise F ST , Table 2) and exhibit a relatively long and slender tail, similar to that of Mahogany Gliders. Additionally, CC samples exhibit a unique signature of selection that differs from both Mahogany and Squirrel Gliders (Fig. 4). It is likely that the gliders from Cape Cleveland are adapted to their local coastal habitat, a peninsula of tropical lowland eucalyptus woodlands, rainforest, and wetlands. The genetically and ecologically distinct CC gliders are therefore potentially qualified as a subspecies of Squirrel Glider with further sampling and analyses. Furthermore, we recorded a high glider density at Cape Cleveland, with a catch rate of 15.4%. This high density, along with their greater genetic diversity compared to other Squirrel Gliders (Table 3), indicates that the CC population is genetically healthy. While genetic admixture exists between the Mahogany Glider and other genetic groups, compelling evidence supports the classification of the Mahogany Glider as at least a highly distinct subspecies. This classification finds support in their status as a monophyletic clade, distinct clustering, and characteristic morphology (e.g., phylogeny, ­­­­Fig. 3; NetView, Fig. 2A; Morphology, Fig. 2C;), which suggest a distinct evolutionary path despite their close genetic relationship to the Squirrel Gliders (­­­­Fig. 3). Morphological features are also unique in Mahogany Gliders, as seen in the FAMD analysis (Fig. 5). These morphological distinctions are consistent with the species description of the Mahogany Glider by Van Dyck (1993) and measurements presented elsewhere (e.g., Ferraro 2012). Furthermore, as for CC gliders, signature of selection analysis suggests potential local adaptation in the Mahogany Glider (Fig. 4). These findings indicate that the Mahogany Glider has potentially adapted in a unique way to the lowland open forests of the Wet Tropics. Further research is needed to fully understand the factors driving local adaptation and its implications for the conservation of this species. Conservation genetics of Mahogany Gliders Genetic diversity of the Mahogany Glider is generally comparable to the non-threatened Squirrel Gliders analysed in this study. Comparisons of heterozygosity based on SNPs, employing similar filtering methods, indicate that the observed heterozygosity (H o ) of the Mahogany Glider is comparable to other threatened marsupials listed in the EPBC Act 1999, such as the Koala, Northern Bettong, Western Barred Bandicoot and Greater Glider (Table 3). However, this range of observed heterozygosity is lower when compared to species with a vulnerable status, such the Greater Bilby, Burrowing Bettong, Long-nosed Potoroo, and Golden Bandicoot (Table 4). Caution should be taken when comparing genetic indices across species, as these indices are heavily dependent on the evolutionary history and population genetics of each species and thus can be influenced by biases introduced through different filters, thresholds, and sample sizes (Schmidt et al. 2021). For instance, despite being categorized as Least Concern in terms of conservation status, the Sugar Glider ( P. breviceps ) and Krefft’s Glider consistently exhibit low observed heterozygosity (Knipler et al. 2022), as also seen for Krefft’s Gliders in our study here (Table S3). Table 4 Observed SNPs heterozygosity (H o ) ranges of selected Australian marsupials with EPBC conservation status. Species EPBC Status H o (range) Reference Mahogany Glider Endangered 0.12 (0.16–0.19) This study Koala Endangered (0.22–0.29) Kjeldsen et al. 2019 Northern Bettong Endangered (0.15–0.22) Todd et al. 2023 Western Barred Bandicoot Endangered (0.14–0.22) White et al. 2018 Greater Glider (south) Endangered 0.14 (0.09–0.21) Knipler et al. 2023 Greater Bilby Vulnerable 0.26 White et al. 2018 Burrowing Bettong Vulnerable (0.18–0.31) White et al. 2018 Long-nosed potoroo Vulnerable 0.34 Mulvena et al. 2020 Golden Bandicoot (mainland) Vulnerable (0.28–0.31) Rick et al. 2023 Red-tailed Phascogale Vulnerable (0.19–0.20) Pierson et al. 2023 Squirrel Glider Least Concern 0.18 Knipler et al. 2021 Sugar Glider ( P. breviceips ) Least Concern (0.15–0.16) Knipler et al. 2022 Structure analyses have revealed two distinct genetic clusters within Mahogany Gliders — the northern and the southern cluster (Fig. 6). The Cardwell Range, situated between these two clusters, appears to serve as a natural barrier. Interestingly, despite this geographical division, genetic admixture persists between the northern and southern groups. This admixture could be a result of natural gene flow through the upper catchment of the Herbert River, or movements across the Herbert River (which is narrow in places). Interestingly, there is a significant northern genetic component in the southern cluster, and this asymmetry cannot be explained based on data to date. The northern cluster is characterized by lower genetic diversity comparing to the southern cluster, a more homogeneous genetic structure (Fig. 8B), and moderate isolation by distance (24% of genetic variation in this cluster is explained by isolation by distance; Fig. S9). Acceptable connectivity of populations may exist through this area, but perhaps only until recently. The catch rate at Muller’s Creek was previously recorded between 7.5–15% in 1995–1996 (Jackson 1998) and around 11.5% in 2008 (personal communication with Mark Parsons, Queensland Government 2020). These high catch rates suggest high glider density in a quality habitat. In contrast, recent catch rate at Muller’s Creek was lower in our study using similar field techniques — just 2% and 3% in 2021 and 2022, respectively (Table S1). Furthermore, individuals from Murray Upper, the northernmost known population of the species, exhibit worryingly low individual heterozygosity (H o and sMLH; Table 3), indicating the population is inbred to some degree. The massive clearance and sugar cane farming in the Tully region from 1880 to 1905 removed and fragmented much suitable habitat (QImaginary 1957; Bolton 1970; The University of Queensland 2018), a situation exacerbated by further clearing over the following decades and widespread habitat damage during Cyclone Yasi (Holloway 2013). However, ongoing forest thickening due to changes in fire regimes (Stanton et al. 2014a; Stanton et al. 2014b) may be a key issue in reducing habitat suitability in recent times in the northern half of the range (Chang et al. 2022). The southern cluster of the Mahogany Glider is genetically more structured across different sampling localities (Fig. 6), and only 10% of genetic variation is explained by isolation by distance (Fig. S9). Therefore, factors other than distance are likely contributing to the population structure. The most likely explanation is habitat fragmentation and hence restricted movement between populations. However, catch rates at some southern sites were high, suggesting considerable abundance within the habitat fragments. Catch rates were high at Bambaroo, Allendale and Easter Creek (Table S1), and these sites had higher genetic diversity than sites in the northern cluster (Table 3). These sites are intriguing given how small these fragments are and low Ne estimates for Bambaroo (Ne = 27.6) and Allendale (Ne = 64.7). This discrepancy may reflect a delay in the loss of neutral genetic diversity because neutral genetic diversity lags behind population isolation and decline (Pinto et al. 2023). The connectivity between Bambaroo and adjacent coastal forest was lost in 1988 (QImaginary 1951; Google Earth imagery through time) and Easter Creek only became fragmented during extensive logging from 1987 to 1997 (QImaginary 1993; Google Earth imagery through time). Given population isolation only occurred in the past 40 years, the full impact of habitat loss and fragmentation on the density and genetic diversity of the Mahogany Gliders in this area is yet to be seen. Conclusion and Management Recommendations This study has shown that the genetics of Mahogany and Squirrel Gliders is complex in Queensland, but that four consensus genetic groups are supported: Mahogany Glider, North Queensland gliders, Cape Cleveland gliders and Squirrel Gliders from mid-eastern/south-eastern Queensland. Evidence of genetic introgression between these groups was found, but whether it is historical or ongoing remains unclear. A first step for conservation is resolving the taxonomic uncertainty associated with these four genetic groups. It is possible that all four groups represent subspecies of the Squirrel Glider, but this requires further genetic and morphological investigation. Sampling should focus on the broad areas of introgression and narrow in on areas of potential contact zones of the four genetic groups. Genetic and phenotypic investigations at these contact zones could resolve the current level of genetic isolation (e.g., Hoskin et al. 2005; Harrison and Larson 2016; Malinsky et al. 2018; Caeiro-Dias et al. 2021) and help resolve the taxonomy. Key sampling areas are located at the southern and western edge of the Mahogany Glider distribution (for contact with NQ and CC), and the Charters Towers–Townsville–Ayr region (for contact between NQ, SQ and CC). In regard to the ongoing conservation of the Mahogany Glider, the genetic and morphological data presented here suggest that this taxon is at least a subspecies, that is, either as a full species or a subspecies of the Squirrel Glider complex. In Australia, subspecies receives the same conservation status as species under the EPBC Act 1999 (Threatened Species Scientific Committee 2023). Given the ongoing threats of habitat loss for the Mahogany Glider, it is imperative that current conservation efforts continue to ensure the long-term survival of these genetically and morphologically distinct gliders. The next critical conservation step involves monitoring population trends and developing tailored conservation strategies for the northern and southern genetic clusters. These strategies should aim to increase population sizes and genetic diversity in the northern cluster and increase connectivity and effective population sizes in the fragmented southern cluster. The latter can be achieved through a combination of assisted gene flow and revegetation of functional habitat corridors. Continuous genetic monitoring of vulnerable populations, particularly those with low effective population sizes and poor genetic diversity, is essential. Additional surveys should continue to better resolve the fine-scale distribution and local densities of the Mahogany Glider, to better understand habitat determinants, connectivity, and target future conservation genetic research. Declarations Acknowledgement We acknowledge the traditional owners of the lands where the fieldwork was conducted: Nywaigi (south of Ingham), Warrgamay (Ingham region), Girringun (Cardwell and Ingham regions), Girramay (Cardwell region), and Gulngay (Tully region). We thank Jacqui Diggins and Terrain Natural Resource Management for the support throughout this project. We thank Jessica Worthington Wilmer and Heather Janetzki from the Queensland Museum for assistance with accessing genetic samples and specimens for measurement, respectively. We are thankful to Daryl Dickson and Mark Parsons for providing additional genetic samples of Mahogany Gliders. Funding for the research was generously provided by Terrain NRM, Holsworth Wildlife Research Endowment, and James Cook University. The dedication and hard work of the many volunteers who assisted with the fieldwork were instrumental to the success of this study, especially the contribution from Chieh Lin and Jonathan Ronnle. We appreciate the expert advice on fieldwork and field site planning provided by Steve Jackson, Nicole Prajbisz, Mark Parsons, Alex Tessieri, and Chris Muriata. The support during fieldwork from rangers of Queensland Parks and Wildlife Services was invaluable, with a particular thanks to Tim Devlin, Joshua Spina, and William White. We thank Megan Higgie, Nicholas Bail, Jordy Groffen (James Cook University), and Paul Ferraro (DEECA) for advice and comments on the results and the manuscript. We thank Marine Lechene for the illustration of the gliders in this manuscript. We acknowledge the communication and support from members of Mahogany Glider Recovery Team and are grateful to the property owners for granting permission to access their properties for the purpose of this study. Lastly, we would like to express our gratitude to Veronica Green for her generous donation of 5 kg of Hinchinbrook Honey and Golden Circle company (Joel Roberts) for donating 80 bottles of raspberry cordial for the surveys. Funding This work was supported by Terrain Natural Resource Management, Holsworth Wildlife Research Endowment, and College of Science and Engineering of James Cook University. Permits and Animal Ethics The research was conducted in accordance with Queensland Department of Environment and Permits for Scientific Research (protected areas: P-PTUKI-100021853; non-protected areas: WA0025939) and James Cook University animal ethics (A2699). Competing Interests The authors have no competing interests to declare that are relevant to the content of this article. Author Contributions Conrad Hoskin and Yiyin Chang contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yiyin Chang. The first draft of the manuscript was written by Yiyin Chang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated and/or analysed during the current study are available in FigShare, DOI TO BE PROVIDED. References Adamack AT, Gruber B (2014). PopGenReport: Simplifying basic population genetic analyses in R. Methods in Ecology and Evolution 5, 384–387. doi:10.1111/2041-210X.12158 Australian Government (2020). 20 mammals by 2020. Department of Agriculture, Water and the Environment. Available at: https://www.environment.gov.au/biodiversity/threatened/species/20-mammals-by-2020 [accessed 25 April 2020] Bender DJ, Contreras TA, Fahrig L (1998). 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Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Published Journal Publication published 30 Apr, 2025 Read the published version in Conservation Genetics → Version 1 posted Editorial decision: Revision requested 03 Nov, 2024 Reviews received at journal 15 Oct, 2024 Reviews received at journal 26 Sep, 2024 Reviewers agreed at journal 24 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers invited by journal 18 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Submission checks completed at journal 06 Sep, 2024 First submitted to journal 06 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5041688","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359195494,"identity":"24f972f7-bcc8-411c-9aa0-8a8c11e6b45c","order_by":0,"name":"Yiyin Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACZgaGA2AEBXIg4sADUrQYg7UkELYLoSWxAUTi02JwnMfwcEHNHXlz/gWMjyvb7qTPDzv8EGiLnZxuAw4th3kMDs849sxw54wHzIZn257lbrydZgDUkmxsdgCPFt6Gw4wbbhxgk2xsO5y7cXYCSMuBxG0EtNgDtbD/BGpJN5yd/oEoLYkbzjewMQK1JMhL5+C3RfIwW8FhnmOHkzfcYGyWbDh32HCDdE7BgQQD3H7hO39482eemsO2G84fPvixoeywvPzs9M0fPlTYyeHSonCAwwDCkgDGCCMb0KlglQbYlYOAfAP7AwiLH6T0D0gEt+pRMApGwSgYmQAAv5Vv5AOtKuwAAAAASUVORK5CYII=","orcid":"","institution":"James Cook University","correspondingAuthor":true,"prefix":"","firstName":"Yiyin","middleName":"","lastName":"Chang","suffix":""},{"id":359195497,"identity":"ebd8ab49-bff8-4b97-b452-68fa8904d9dd","order_by":1,"name":"Lorenzo V. Bertola","email":"","orcid":"","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"V.","lastName":"Bertola","suffix":""},{"id":359195499,"identity":"7d930596-7248-4b33-ace5-ce755e7146e5","order_by":2,"name":"Kyall R. Zenger","email":"","orcid":"","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Kyall","middleName":"R.","lastName":"Zenger","suffix":""},{"id":359195500,"identity":"d29d7091-11ed-42d6-a5aa-155f133e1b9f","order_by":3,"name":"Conrad J. Hoskin","email":"","orcid":"","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Conrad","middleName":"J.","lastName":"Hoskin","suffix":""}],"badges":[],"createdAt":"2024-09-06 05:24:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5041688/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5041688/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10592-025-01700-7","type":"published","date":"2025-04-30T15:57:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66301260,"identity":"279fa712-a73b-4057-8480-d8f1c28a9ec2","added_by":"auto","created_at":"2024-10-10 05:58:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8692756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGeographical distribution of sampling localities in (A) Queensland and (B) the Wet Tropics region. Symbols and colours represent different glider species: Squirrel Glider (blue); Mahogany Glider (red); Krefft’s Glider (green). The illustrations of the glider, from the smallest to the largest, are Krefft’s Glider (map A), Squirrel Glider (map A), and Mahogany Glider (map B). The illustrations were created by Marine Lechene.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/870b5d5e729d18506a66d2d3.jpg"},{"id":66301265,"identity":"e708a270-c110-4c3f-9d10-f06ed7439131","added_by":"auto","created_at":"2024-10-10 05:58:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1127633,"visible":true,"origin":"","legend":"\u003cp\u003eStructure analyses of Mahogany Glider (\u003cem\u003eP. gracilis\u003c/em\u003e), Squirrel Glider (\u003cem\u003eP. norfolcensis\u003c/em\u003e), and Krefft’s Glider (\u003cem\u003eP. notatus\u003c/em\u003e): (A) NetView networks for the neutral dataset of the three species. Individuals are coloured based on sampling locality. Krefft’s Gliders are depicted in greens, Mahogany Gliders in reds, and Squirrel ­­Gliders in blues. (B) DAPC for all three glider species (left) and Mahogany and Squirrel Gliders only (right). (C) STRUCTURE plots for all three glider species (top) and Mahogany and Squirrel Glider only (bottom), with optimal and second optimal clustering identified by the ∆K method. See Fig. S4 for STRUCTURE plots exclusively for Mahogany and Squirrel Gliders, encompassing K values ranging from 2 to 10.\u003c/p\u003e","description":"","filename":"Fig2PetaurusGeneticStructure.png","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/06ce59bedde028d590b9a072.png"},{"id":66301262,"identity":"d7bdc645-f833-4993-9522-43c65d8deb74","added_by":"auto","created_at":"2024-10-10 05:58:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":999732,"visible":true,"origin":"","legend":"\u003cp\u003eThe maximum likelihood phylogenetic tree of all glider samples. Krefft’s Gliders form a distinct clade, and four subclades were found in Mahogany and Squirrel Glider clade: North Queensland (NQ), Mahogany Gliders (MG), Cape Cleveland (CC) and mid-eastern/south-eastern Queensland individuals (SQ). The Mahogany Glider clade is further split into northern (brown) and southern (orange) individuals. The unit for the tree scale represents the number of substitutions per site. The tree was built using IQ-TREE v2.2.2.2 (Minh \u003cem\u003eet \u0026nbsp;al.\u003c/em\u003e 2020) with 30,000 bootstrap replicates and was rooted with Krefft’s Glider in iTOL (Letunic and Bork 2021).\u003c/p\u003e","description":"","filename":"Fig3PhylogeneticTree.png","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/1cdea6d6fdb2635c5fc1caa0.png"},{"id":66301259,"identity":"c22ba844-49d7-4c95-a1ca-ca49d5717125","added_by":"auto","created_at":"2024-10-10 05:58:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60304,"visible":true,"origin":"","legend":"\u003cp\u003eThe Discriminant Analysis of Principal Components (DAPC) of outlier loci from Mahogany and Squirrel Gliders. The individuals are marked by the four consensus genetic groups based on genetic structure. Mahogany Gliders (MG) are represented in red, North Queensland individuals (NQ) and Cape Cleveland individuals (CC) in light blue, and mid-eastern/south-eastern Queensland individuals (SQ) in dark blue. The eigenvalues and the total variance contribution (var. cont.) of the DAPC plot are displayed in the top right corner.\u003c/p\u003e","description":"","filename":"Fig4OutlierDAPC.png","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/f7798c11e2889dda5e6a5fdb.png"},{"id":66301263,"identity":"ea87ed79-b56a-40b7-88c1-49acc7bce662","added_by":"auto","created_at":"2024-10-10 05:58:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":330528,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots (A) and Factor Analysis of Mixed Data (FAMD) (B) for morphological traits for Mahogany and Squirrel Glider consensus genetic groups. The boxplots illustrate body length (cm), tail length (cm), and tail-to-body length ratio for the four consensus genetic groups: Mahogany Gliders (MG), North Queensland individuals (NQ), Cape Cleveland individuals (CC), and mid-eastern/south-eastern Queensland Squirrel Gliders (SQ). Sample sizes for each measurement are indicated under the boxplot. The FAMD plot (B) demonstrates the four genetic groups based on five morphological measurements: body length, tail length, head length, head width, and tail character.\u003c/p\u003e","description":"","filename":"Fig5MorphologyFAMD.png","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/12364b3f8608f9aa14b390c3.png"},{"id":66301264,"identity":"9705f020-53f5-45ea-ae9f-f5fcc96fdb5f","added_by":"auto","created_at":"2024-10-10 05:58:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2988463,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation genetic structure of Mahogany Gliders. (A) Discriminant Analysis of Principal Components (DAPC) across all sampling localities, with the first DA eigenvalue (PC1, x-axis) explaining most of the variation. (B) STRUCTURE analysis illustrating the optimal (K = 2) and second optimal (K = 6) clustering, with sampling localities arranged from south (left) to north (right). (C) Map of sampling localities, with the Herbert River marked in blue. The red dashed line in plots A, B, and C signifies the separation between northern and southern populations based on optimal clustering results. Asterisks on the map in panel C show sampling sites with trapping success (Table S1).\u003c/p\u003e","description":"","filename":"Fig6MGstructure.png","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/5080a1588bd75df8c7296512.png"},{"id":81987471,"identity":"bd30f783-c9d7-4ea2-a273-94368c633b35","added_by":"auto","created_at":"2025-05-05 16:03:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15108897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/1a6bcda5-2663-4fe4-ac07-a3458d3c679a.pdf"},{"id":66301576,"identity":"f49a4618-56e8-40ca-8344-90eacf90c016","added_by":"auto","created_at":"2024-10-10 06:06:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1303066,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5041688/v1/493bd215525cbe549fab8327.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conservation genetics of Mahogany Gliders and insights into their evolutionary relationship with Squirrel Gliders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHabitat destruction poses a significant threat to wildlife species, with estimates suggesting this threat impacts nearly 90% of threatened species and is the primary driver of extinction (Hogue and Breon 2022). Loss of habitat often leads to fragmentation, hence transforming once-interconnected habitats into smaller, isolated patches. In these fragmented landscapes, wildlife populations decline and become isolated, suffering from harmful edge effects, habitat degradation, and a loss of connectivity (Bender \u003cem\u003eet al.\u003c/em\u003e 1998; Laurance \u003cem\u003eet al.\u003c/em\u003e 2007; Didham 2010). Small, isolated populations are more vulnerable to genetic stochasticity, which can lead to the loss of genetic diversity and inbreeding depression (Willi \u003cem\u003eet al.\u003c/em\u003e 2007; Frankham \u003cem\u003eet al.\u003c/em\u003e 2017; Lino \u003cem\u003eet al.\u003c/em\u003e 2019). Ultimately, loss of genetic diversity diminishes a species\u0026rsquo; adaptive potential to environmental changes (Hedrick 2000; Charlesworth and Willis 2009) and can lead to extinction (Frankham 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIntegrating genetic tools into conservation efforts enhances our understanding of habitat loss and fragmentation impacts, facilitating targeted management strategies (Luikart \u003cem\u003eet al.\u003c/em\u003e 2003; Kohn \u003cem\u003eet al.\u003c/em\u003e 2006). One powerful tool is the analysis of Single Nucleotide Polymorphisms (SNPs), which uses thousands of specific genome positions with nucleotide substitutions to provide high-resolution information on individual differences, population structure, and genetic diversity (Marth \u003cem\u003eet al.\u003c/em\u003e 1999; Schork \u003cem\u003eet al.\u003c/em\u003e 2000; Primmer 2009; Steiner \u003cem\u003eet al.\u003c/em\u003e 2013). The results can then be used to target conservation efforts, such as identifying areas for revegetation to improve population connectivity (e.g., \u003cspan lang=\"EN-AU\"\u003eBertola et al. 2023)\u003c/span\u003e and detecting local adaptation to environment change (e.g., McCulloch et al. 2021; Giska et al. 2022). Importantly, genomic tools can also reveal cryptic species\u0026nbsp;(Dufresnes \u003cem\u003eet al.\u003c/em\u003e 2019) and help resolve taxonomic uncertainty, which is crucial because conservation efforts are focussed on formally designated taxa (Dufresnes et al. 2019, 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Squirrel Glider (\u003cem\u003ePetaurus norfolcensis\u003c/em\u003e) and the Mahogany Glider (\u003cem\u003ePetaurus gracilis\u003c/em\u003e) are both medium-sized gliding possums inhabiting eucalyptus open forests in eastern Australia. Squirrel Gliders are distributed from Victoria to North Queensland,\u0026nbsp;while Mahogany Gliders are restricted to a 120 km stretch of wet sclerophyll lowland forests in North Queensland between Ingham and Tully\u0026nbsp;(Van Dyck \u003cem\u003eet al.\u003c/em\u003e 2013). The distributions of the two species are not known to overlap (Van Dyck 1993; Jackson and Claridge 1999; Goldingay and Jackson 2004; Sharpe and Goldingay 2010). Initially considered a subspecies of the Squirrel Glider (Iredale and Troughton 1934), the Mahogany Glider was reclassified as a distinct species due to morphological differences (Van Dyck 1993). The Mahogany Glider is larger, with a body length of 215\u0026ndash;265 mm and weight of 255\u0026ndash;500 g, compared to the Squirrel Glider\u0026rsquo;s 180\u0026ndash;244 mm length and 173\u0026ndash;300 g weight. It also has a longer, less fluffy tail (300\u0026ndash;390 mm vs. 220\u0026ndash;300 mm) (Van Dyck 1993; Jackson 2011; Jackson and Schouten 2012). In skull morphology, the Mahogany Glider has a narrower interorbital width but larger skull length, rostral height, and zygomatic width (Van Dyck 1993; Jackson 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies have observed morphological and body size variation in Squirrel Gliders but data from gliders in eastern and northern Queensland is limited (Stobo-Wilson \u003cem\u003eet al.\u003c/em\u003e 2020). The genetic differentiation between Mahogany and Squirrel Gliders also remains unresolved. Earlier research using two mitochondrial genes (ND2 and ND4) and two nuclear markers (\u0026omega;-globin and ApoB gene) showed low sequence divergence (1.8\u0026ndash;2.2%) between the species, and the phylogeny displayed a single admixed clade (Malekian et al. 2010; Ferraro 2012). No detailed genomic assessment of genetic differentiation and relatedness between the two species has been conducted to date, thereby creating taxonomic uncertainty that hinders accurate species identification and effective conservation strategies.\u003c/p\u003e\n\u003cp\u003eThe Mahogany Glider is listed as Endangered under the Environment Protection and Biodiversity Conservation (EPBC) Act of 1999. Within its small distribution, the glider relies on mature lowland forests with large, diverse trees that provide essential tree hollows and year-round flower nectar (Jackson 2000). Agricultural deforestation, particularly for sugarcane and cattle farming, has reduced its habitat by approximately 40% (Jackson \u003cem\u003eet al.\u003c/em\u003e 2011; Jackson \u003cem\u003eet al.\u003c/em\u003e 2019). Furthermore, suitable habitat is now fragmented and includes more than 400 habitat patches smaller than 1 km\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(Chang \u003cem\u003eet al.\u003c/em\u003e 2022). Habitat loss and severe fragmentation can reduce and structure genetic diversity and may have resulted in poor genetic consequences in some isolated, small populations(Frankham \u003cem\u003eet al.\u003c/em\u003e 2010). Understanding genetic diversity across populations of the Mahogany Glider is a key priority in the recovery plan (Parson and Latch 2006) and other conservation assessments (Curtis 2012; Burbidge \u003cem\u003eet al.\u003c/em\u003e 2014). Despite the recognition of the importance of population genetic assessments to efficiently manage threatened species (e.g., Frankham et al. 2017), none has been conducted for the Mahogany Glider.\u003c/p\u003e\n\u003cp\u003eHere, we present the first detailed population genetic assessment of the Mahogany Glider, and we place this within the broader context of Squirrel Glider populations in Queensland. Our aims were to: (1) investigate the evolutionary relationship and key morphological characteristics between Mahogany and Squirrel Gliders, and (2) assess the population genetic structure and genetic diversity of the threatened Mahogany Glider. The results provide a foundation for resolving the taxonomy of Mahogany and Squirrel Gliders in Queensland and inform the conservation management of Mahogany Gliders.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eFieldwork \u0026mdash; surveys and sample collection\u003c/h3\u003e\n\u003cp\u003eA total of 16 trapping surveys were conducted to collect tissue samples from Mahogany Gliders (\u003cem\u003ePetaurus gracilis\u003c/em\u003e) and Squirrel Gliders (\u003cem\u003eP. norfolcensis\u003c/em\u003e) at 14 distinct sites between 15\u003csup\u003eth\u003c/sup\u003e April 2021 and 31\u003csup\u003est\u003c/sup\u003e August 2022, for a total of 1,525 trap nights (Fig. 1; Table S1). We also collected tissue samples from Krefft\u0026rsquo;s Gliders (\u003cem\u003eP. notatus\u003c/em\u003e), a smaller glider species that is sympatric with Mahogany and Squirrel Gliders.\u003c/p\u003e\n\u003cp\u003eAt each site, we strapped 20 wire cage traps (56 x 20 x 20 cm) to trees 2.5\u0026ndash;4 m above the ground and 100\u0026ndash;400 m apart depending on habitat size and suitability. The back half of each cage trap was covered with waterproof plastic sheeting for rain shelter. Each trap contained a bait ball made of peanut butter, honey and oats to attract gliders to the traps (Jackson 2001; Knipler \u003cem\u003eet al.\u003c/em\u003e 2021). We squeezed additional honey on top of the bait ball to increase bait smell and keep the bait moist. We also sprayed a solution of water, raspberry cordial and honey above the trap as a scent lure. Traps were baited and opened just before sunset (5 pm) and checked at 11 pm and each morning before sunrise (5 am). Captured individuals were weighed, sexed, and measured (head length and width, body length, and tail length). We collected a tissue biopsy sample from the edge of an ear using a small ear punch and preserved the sample in 90% ethanol. We took photos of the face from the front and the side, and the whole body next to a scale bar. The glider was then released at the point of capture. All equipment was sterilized using 70% ethanol after each capture.\u003c/p\u003e\n\u003cp\u003eA total of 44 Mahogany Gliders, 6 Squirrel Gliders and 9 Krefft\u0026rsquo;s Gliders samples were trapped and sampled in the field surveys (Table S1). Five of the 16 surveys conducted yielded no captures. For the sites with catching success, trapping rates were generally low, ranging from 0.8% to 15%. The highest trapping rates were observed in the northern section of Paluma Range National Park, particularly at Bambaroo, Easter Creek, and Allendale (Fig. 1; Table S1). Additional samples were obtained from five rescued Mahogany Gliders (via wildlife-carer Daryl Dickson, MGDD01\u0026minus;05, Table S2) and from a previous trapping survey during 2008 and 2010 conducted by Queensland Parks and Wildlife Service (via Mark Parsons, MGMP01\u0026minus;08, Table S2). We also included historical samples of 10 Mahogany Glider and 43 Squirrel Glider from the Queensland Museum (Table S2). The Squirrel Glider samples extended from southeastern to far north Queensland (Fig. 1). The museum samples, collected between 1989 and 2017, were sourced from fur, skin, liver, or muscle specimens that were obtained from field-specimens, rescued individuals, or deceased animals.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSNP genotyping and filtering\u003c/h3\u003e\n\u003cp\u003eA total of 125 tissue samples were genotyped: 67 from Mahogany Gliders, 49 from Squirrel Gliders, and 9 from Krefft\u0026rsquo;s Gliders (Fig. S1; Table S2). DNA extraction and SNP genotyping were performed at Diversity Arrays Technology (DArTseq) in Canberra, using the DArTseq method with PstI and Sphlv4 restriction enzymes. Genomic DNA was extracted using the Macherey-Nagel NucleoMag Plant kit and subjected to high-density sequencing (2.5 million reads per individual) on an Illumina NovaSeq 6000 S2 flow cell, referencing the \u003cem\u003ePetaurus\u003c/em\u003e DArTseq (1.0) genomic library (Jaccoud \u003cem\u003eet al.\u003c/em\u003e 2001; Kilian \u003cem\u003eet al.\u003c/em\u003e 2012). Single Nucleotide Polymorphism (SNP) calling was performed using the DArTsoft14 algorithm within the KDCompute pipeline developed by Diversity Arrays Technology (http://www.kddart.org/kdcompute.html).\u003c/p\u003e\n\u003cp\u003eWe performed SNP quality control using a customized R script (Fig. S1; ESM1) and dartR v2.7.2 (Gruber et al. 2018) to ensure standardized sequence quality (R v4.2.2; R Development Core Team 2022; Rstudio team 2023). Individuals with more than 35% missing genotypes (i.e., a low call rate) and single nucleotide polymorphisms (SNPs) with more than 10% missingness were removed. To ensure reliable and consistent genotyping results, SNPs with extreme read depth (\u0026lt;10 \u0026amp; \u0026gt;50) and reproducibility (consistency of SNPs calling result) lower than 0.99 were also removed. Secondary SNPs (i.e., SNPs called from the same locus) were removed by retaining the SNP with the higher reproducibility.\u003c/p\u003e\n\u003cp\u003eWe further applied minor allele count, linkage disequilibrium, and outlier loci filters to both the complete dataset and the species-specific datasets. Briefly, singleton SNPs (minor allele count equals to one) were removed (O\u0026rsquo;Leary \u003cem\u003eet al.\u003c/em\u003e 2018). The loci under linkage disequilibrium were filtered out with a threshold of 0.9 using PLINK v1.90b6.26 (Purcell \u003cem\u003eet al.\u003c/em\u003e 2007) and\u0026nbsp;bigsnpr\u0026nbsp;v1.11.6 (Purcell \u003cem\u003eet al.\u003c/em\u003e 2007; Prive \u003cem\u003eet al.\u003c/em\u003e 2018), retaining only one of the linked markers with higher call rate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify loci under selection, we conducted outlier analysis using three distinct methods: OutFLANK v0.2 (Whitlock and Lotterhos 2015), Bayescan v2.1 (Foll and Gaggiotti 2008), and pcadapt v4.3.3 (Luu \u003cem\u003eet al.\u003c/em\u003e 2017). The results from OutFlank and Bayescan did not reveal any outlier loci. Using pcadapt, we discovered the outlier loci that significantly contribute to the genetic structure. We performed principal component analyses with a false discovery rate of 0.01. Outlier loci were then filtered based on their significance using two thresholds: the highly conservative Bonferroni method and the moderately conservative Benjamini-Hochberg method (Luu \u003cem\u003eet al.\u003c/em\u003e 2017). To maximize the information retained, we created two distinct datasets based on the outlier filter: (1) a dataset containing only neutral loci, with outlier loci removed using the Bonferroni method, for genetic structure analysis; (2) a separate dataset comprising solely outlier loci, identified through the Benjamini-Hochberg method, for signatures of selection analysis.\u003c/p\u003e\n\u003cp\u003eFirst-degree relatives and potentially duplicated samples were identified using the KING method of moments in SNPRelate v1.32.0\u0026nbsp;(Manichaikul et al. 2010; Zheng et al. 2012). For first-degree relatives, only the individual with the highest call rate was retained for genetic structure and diversity analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDownstream analyses were conducted in two steps. First, to better understand the evolutionary relationship between Mahogany and Squirrel Gliders, population genetic structure was assessed with a dataset including all three species (Fig. S1). For each of the identified genetic clusters, we then evaluated genetic distance, genetic diversity, morphological characteristics, and signatures of selection. Second, we conducted a conservation genetic assessment focusing on the Mahogany Glider.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferent datasets were used based on the assumptions and information required for each analysis (Funk et al. 2012) (Table 1). Phylogenetic analyses were performed on all loci, including both neutral and outlier loci. Genetic structure and diversity analyses were conducted on neutral loci only, while signature of selection analysis was investigated with outlier loci only. Therefore, the SNPs number varied across datasets. An overview of the analysis workflow is presented in Table 1, and the detailed steps to produce the different datasets are summarised in Fig. S1. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Overview of the genetic analyses presented in this study, highlighting the dataset used. The table details the dataset used for each type of analysis for (i) all gliders, (ii) Mahogany and Squirrel Gliders together (MG-SQ), (iii) Mahogany Gliders only (MG), and (iv) Squirrel Gliders only (SQ).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAnalysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 425px;\"\u003e\n \u003cp\u003eSpecies/Loci subset\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAll gliders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMG-SQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eDAPC, STRUCTURE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNetView\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAMOVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAll Loci\u003c/p\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003cp\u003eOutlier Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003cp\u003eOutlier Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eMantel test\u003c/p\u003e\n \u003cp\u003e(Isolation by distance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eGenetic differentiation (F\u003csub\u003eST\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eGenetic diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eNeutral Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePhylogenetic Tree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAll Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSignature of Selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eOutlier Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eOutlier Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1. Genetic and morphological assessment of Mahogany and Squirrel Gliders\u003c/p\u003e\n\u003ch4\u003eInterspecific population structure\u003c/h4\u003e\n\u003cp\u003eTo identify genetically distinct populations, we analysed genetic structure with a dataset of neutral loci for all three \u003cem\u003ePetaurus\u003c/em\u003e species sampled (N = 86).\u003c/p\u003e\n\u003cp\u003eNetView uses the k-nearest neighbours (kNN) approach to visualize genetic distance matrices (Neuditschko \u003cem\u003eet al.\u003c/em\u003e 2012). These matrices were computed using three distinct methods: Euclidean distance applied on allele frequency within individuals (eucl) (Jombart and Ahmed 2011), pairwise difference on number of loci for which individuals differ (nLoci) (Paradis and Schliep 2019), and number of allelic differences between two individuals (nAllele) (Kamvar \u003cem\u003eet al.\u003c/em\u003e 2014).\u003c/p\u003e\n\u003cp\u003eWe used the Discriminant Analysis of Principal Components (DAPC) and the unsupervised membership grouping in adegenet v2.1.8 to visualize genetic clustering patterns without assumptions about sampling localities (Jombart and Collins 2015). DAPC reduces the dimensionality of genetic data to identify the underlying population structure (Jombart \u003cem\u003eet al.\u003c/em\u003e 2010). The unsupervised membership grouping on the sampling localities were compared and visualized using K-means clustering. In response to recent critiques of PCA in genetic analyses (REF), we reported the variance explained by the first two PCs and used additional methods to confirm the population structure.\u003c/p\u003e\n\u003cp\u003eTo investigate genetic structure and admixture jointly, we used the Bayesian clustering method of\u0026nbsp;STRUCTURE v2.3.4 (Pritchard \u003cem\u003eet al.\u003c/em\u003e 2000; Falush \u003cem\u003eet al.\u003c/em\u003e 2003; Falush \u003cem\u003eet al.\u003c/em\u003e 2007; Hubisz \u003cem\u003eet al.\u003c/em\u003e 2009). Ten replicates for each K value ranging from 1 to 10 were performed and the results were extracted using\u0026nbsp;pophelper v2.3.1 (Francis 2017). The optimal K value was determined by identifying the peak of ∆K in the Evanno plots (Evanno \u003cem\u003eet al.\u003c/em\u003e 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIsolation by distance for each of Mahogany and Squirrel Gliders was investigated by testing correlations between geographical Euclidean distance and pairwise individual genetic distance (proportion of alleles shared) using Mantel tests (Gruber et al. 2018). The results of Mantel tests were then visualized using MASS v.7.3 (Kemp 2002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic differentiation (F\u003csub\u003eST\u003c/sub\u003e) analyses were performed to assess the extent of variation explained between the four genetic groups based on neutral loci. These analyses were performed using the bootstrapped method (hierfstat v0.5, Goudet 2005) and Analysis of Molecular Variance (AMOVA) (poppr v2.9.3, Kamvar et al. 2014).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003ePhylogenomics\u003c/h4\u003e\n\u003cp\u003eTo clarify the phylogenetic relationships between Mahogany and Squirrel Gliders, we constructed a maximum likelihood phylogenetic tree using IQ-TREE v2.2.2.2 (Minh \u003cem\u003eet al.\u003c/em\u003e 2020). The tree was built using both neutral and outlier loci with monomorphic loci and missing data removed (N = 6,501) (Fig. S1). We used ModelFinder Plus in IQ-TREE to identify the substitution model (Kalyaanamoorthy \u003cem\u003eet al.\u003c/em\u003e 2017). A maximum likelihood tree was then computed using the selected substitution model (TVM+F+I+G4), in conjunction with the ultrafast bootstrap method with 30,000 replicates. The resulting phylogenetic tree was visualized using iTOL (Letunic and Bork 2021), with Krefft\u0026rsquo;s Gliders rooted as the outgroup based on available literature (Malekian \u003cem\u003eet al.\u003c/em\u003e 2010; Cremona \u003cem\u003eet al.\u003c/em\u003e 2020) and the population structure analyses of this study.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eAnalysis of signatures of selection among consensus genetic groups\u003c/h4\u003e\n\u003cp\u003eIndividual loci that significantly deviate from average genome-wide population divergence patterns may indicate the presence of selection. Therefore, we assessed potential local adaptation of the identified genetic groups using the outlier loci dataset derived from the SNP genotyping and filtering section above. We used DAPC in R package adegenet v2.1.8 to assess clustering patterns of individuals based on the outlier loci (Jombart and Collins 2015).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eMorphological assessment of consensus genetic groups\u003c/h4\u003e\n\u003cp\u003eOnce the consensus genetic groups were identified across the above analyses, we assessed morphological differences among them. We measured body length (snout-vent length) and tail length on live individuals captured in the field and on specimens housed in the Queensland Museum (Brisbane). The museum specimens included both wet (spirit) and dry (skin) specimens. Additionally, head length and head width were taken when specimens contained skulls. We conducted a Factor Analysis of Mixed Data (FAMD) for the total of 118 Mahogany Glider and 79 Squirrel Gliders. The FAMD analysis integrated both categorical (specimen type, sex, tail character) and continuous data (body, tail, head length and width) into a principal component analysis (Kassambara 2016). To address missing values, the regression method from R package missMDA v1.19 was applied (Husson and Josse 2023). The analysis was conducted using the FactoMineR v2.9 (L\u0026ecirc; \u003cem\u003eet al.\u003c/em\u003e 2008) and factoextra v1.07 (Kassambara and Mundt 2020).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2. Conservation genetic analyses of Mahogany Gliders\u003c/h3\u003e\n\u003cp\u003eThe genetic analyses in this section were based on the Mahogany Glider-only data. Genetic diversity was assessed based on neutral loci and the data was filtered to remove markers monomorphic for Mahogany Gliders. Population genetic structure was assessed following the methodology described in the interspecific population structure section above. Additionally, the effective population size for each sampling locality was estimated using the linkage disequilibrium method (and assuming a monogamous mating system) in NeEstimator v2.1 (Jackson 2000b; Do et al. 2014). Only Mahogany Glider samples collected between 2017 and 2022 were used, to prevent overlapping generations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the conservation genetics of Mahogany Gliders, we compared genetic diversity of Mahogany Gliders to that of the consensus genetic groups in Squirrel Gliders. We quantified individual genetic diversity using observed and expected heterozygosity (H\u003csub\u003eo\u003c/sub\u003e/H\u003csub\u003ee\u003c/sub\u003e) and standardized multi-locus heterozygosity (sMLH), as per the methodology in dartR v2.9.7 (Gruber et al. 2018) and inbreedR v0.3.3 (Stoffel \u003cem\u003eet al.\u003c/em\u003e 2016), respectively. The genetic diversity of each sampling locality, consensus genetic group, and species was assessed using several indices, including averaged sMLH, H\u003csub\u003eo\u003c/sub\u003e/H\u003csub\u003ee\u003c/sub\u003e, Wright\u0026rsquo;s inbreeding index F\u003csub\u003eIS\u003c/sub\u003e (Gruber et al. 2018), and allelic richness (Ar) corrected using the rarefaction method (Adamack and Gruber 2014). To ensure the accurate estimation of heterozygosity, any loci with missing data were excluded (Schmidt et al. 2021). Additionally, we included monomorphic loci to examine their effect on the estimation of genetic diversity (Schmidt et al. 2021).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eSNP genotyping and filtering \u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eDArTSeq genotyping identified 67,261 single nucleotide polymorphisms (SNPs) across 115 individuals from all three species (Fig. S1). For nine samples, DNA extraction was unsuccessful. The quality control process, which considered call rate, reproducibility, secondary loci, and read depth, filtered out low-quality loci and removed an additional 18 low-quality samples (16 from museums and 2 from old field collections). Most of the failed museum samples were fur samples (Fig. S2). After these steps, 97 individuals remained with 10,408 SNPs.\u003c/p\u003e\n\u003cp\u003eFiltering based on minor allele count and linkage disequilibrium excluded 3,455 loci from the all-species dataset, 948 loci from the Mahogany Glider dataset, 1,598 loci from the Squirrel Glider dataset, and 2,514 loci from the Krefft\u0026rsquo;s Glider dataset. Outlier analysis identified 349, 37, and 32 loci as outliers in the all-species dataset, Mahogany Glider dataset, and Squirrel Glider dataset, respectively (Table 1). No outliers were identified in the Krefft\u0026rsquo;s Glider dataset because of the low sample size of nine individuals.\u003c/p\u003e\n\u003cp\u003eKinship analyses revealed the presence of three pairs of duplicates (kinship coefficient \u0026gt; 0.354, \u0026nbsp;(Manichaikul et al. 2010) indicating three individuals were sampled in the field twice. Additionally, six pairs of first-degree relatives (kinship coefficient \u0026gt; 0.16) were identified among the Mahogany Glider samples in Bambaroo and Easter Creek, as well as one triplet of first-degree relatives among Squirrel Glider samples from near Airlie Beach. Among the first-degree relatives, the individual with the highest call rate was retained for genetic structure and diversity analyses.\u003c/p\u003e\n\u003cp\u003eThe number of SNPs in each of the analysis datasets was: 9,258 for all-species dataset (N = 97), 9,651 for Mahogany-Squirrel Glider dataset (N = 88), 9,719 for Mahogany Glider dataset (N = 58), and 8,941 for Squirrel Glider dataset (N = 30).\u003c/p\u003e\n\u003ch3\u003e1.\u0026nbsp; \u0026nbsp;Genetic and morphological assessment ofMahogany and Squirrel Gliders\u003c/h3\u003e\n\u003ch4\u003eInterspecific population structure\u003c/h4\u003e\n\u003cp\u003eIn all analyses, Krefft\u0026rsquo;s Gliders from the Wet Tropics formed a highly distinct group compared to Mahogany and Squirrel Gliders. In the NetView analyses, Krefft\u0026rsquo;s Gliders were not joined to any other species, even when the nearest neighbours (kNN) parameter was set to 30 (Fig. 2A; Fig. S3). In the DAPC analyses, Krefft\u0026rsquo;s Gliders were identified as a highly distinct group that is well-distinguished from the other two species with a high eigenvalue (7608 with all loci, 2927 with neutral loci) (Fig. 2B). In the STRUCTURE analyses, Krefft\u0026rsquo;s Glider also emerged as genetically distinct, showing no evidence of genetic admixture with other species (Fig. 2C). Results of AMOVA also showed that the outlier loci (N = 542) explained 70% of variation when Krefft\u0026rsquo;s gliders were included (Fig. S7). Therefore, below we present results based on neutral loci of Mahogany and Squirrel Glider only.\u003c/p\u003e\n\u003cp\u003eThe genetic structure of Mahogany Gliders exhibits some degree of differentiation from Squirrel Gliders, although the extent of this differentiation varies across different analyses. NetView analyses utilizing Euclidean distance matrices (eucl) consistently delineated Mahogany and Squirrel Gliders into two distinct groups (Fig. S3), maintaining separation even up to a kNN value of 42. Conversely, the other two distance matrices (nLoci and nAllele) demonstrated a convergence between Squirrel and Mahogany Gliders at kNN values of 15 and 25, respectively. DAPC distinguished Mahogany and Squirrel Gliders with a high eigenvalue of 1890 (Fig. 2B), although\u0026nbsp;only 37% of total variance was explained. STRUCTURE analyses also suggested an optimal clustering at K = 2, demarcating the Mahogany Glider from the Squirrel Glider (Fig. 2C; Fig. S5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree distinct groups of Squirrel Gliders were identified: (1) gliders from the mid-eastern and south-eastern regions of Queensland, extending from Brisbane to Charters Towers (SQ), (2) gliders from Cape Cleveland (CC), and (3) gliders from Chillagoe, Tolga, and Princess Hills north of Townsville (NQ) (Fig. 2). The SQ group exhibited considerable variation and consistently formed its own cluster, distinct from the Mahogany Gliders (Fig. 2). The assignment of the CC group varied across analyses. In most structure analyses, these gliders were grouped with the Squirrel Gliders (Fig. 2A; Fig. S4A), but in some analyses, they formed their own distinct cluster (Fig. 2B, C; Fig. S4A, B). The NQ samples, which comprise a broad distribution from Townsville to Chillagoe in north Queensland, were particularly interesting. Unlike the SQ group, these samples were grouped with Mahogany Gliders in most of the structure analyses, rather than with Squirrel Gliders (Fig. 2A, B; Fig. S4A).\u003c/p\u003e\n\u003cp\u003eDespite the clear genetic distinction between Mahogany Gliders and Squirrel Gliders, evidence of introgression between the two species is evident. A gradient of admixture is observed in Squirrel Gliders from north of Mackay to the southern and northwestern range of the Mahogany Glider (Fig. 2C). In the STRUCTURE analysis (K = 2), minor introgression is detected between Mahogany Gliders (MG) and Squirrel Gliders north of Mackay, but more than half of the genetic composition of the CC and NQ groups originates from Mahogany Gliders (Fig. 2C). The NQ group, identified as Squirrel Gliders based on morphology and collection localities, were connected with Mahogany Gliders in the NetView analysis at kNN = 30 (Fig. 2A). This pattern persisted in the unsupervised membership grouping, where these samples consistently clustered with Mahogany Gliders (Fig. S4). Even in the STRUCTURE analysis (K = 2 and K = 5), the NQ samples predominantly displayed genetic components from Mahogany Gliders, with some admixture from CC (Fig. 2C).\u003c/p\u003e\n\u003ch4\u003ePhylogenomic analysis further support recognition of four genetic groups\u003c/h4\u003e\n\u003cp\u003eThe maximum likelihood tree based on the all-species dataset using both neutral and outlier loci conforms with the population genetic results presented above (Fig. 3; Fig. S1). Krefft\u0026rsquo;s Gliders form a distinct and divergent group, while the relationships among Mahogany and Squirrel Gliders are complex. All Mahogany Glider samples cluster into a single clade, yet this clade is nested within the broader Squirrel Glider clade. Within this broader clade, the North Queensland (NQ) Squirrel Gliders are the most divergent group, forming a sister clade to the clade that includes the subclades of Mahogany Glider, Cape Cleveland Squirrel Gliders (CC), and mid-eastern/south-eastern Queensland Squirrel Gliders (SQ). These clades and subclades have high bootstrap support (98\u0026ndash;100; \u0026shy;\u0026shy;\u0026shy;\u0026shy;Fig. 3). Additionally, the Mahogany Glider clade is divided into two groups: a northern group (North MG) and a southern group (South MG), separated by the Cardwell Range.\u003c/p\u003e\n\u003cp\u003e2). Notably, the F\u003csub\u003eST\u003c/sub\u003e estimates between Mahogany Gliders and the three Squirrel Glider groups (NQ, CC, and SQ) were lower (0.07\u0026ndash;0.14) compared to the differentiation observed among the three Squirrel Glider groups themselves (0.17\u0026ndash;0.19;\u0026nbsp;Table 2)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Pairwise F\u003csub\u003eST\u003c/sub\u003e calculated based on neutral loci (lower unshaded diagonal) between the four consensus genetic groups as determined by the results of population genetic structure and phylogenetic analyses. These groups are Mahogany Gliders (MG), the North Queensland individuals (NQ), the Cape Cleveland individuals (CC), and the remaining Squirrel Gliders (SQ).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003csub\u003eST\u003c/sub\u003e/ Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eSQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003eSignatures of selection\u003c/h4\u003e\n\u003cp\u003eIn the analysis of molecular variance (AMOVA) of the all-species dataset, the outlier loci explained around 65% of the variance between species, whereas only about 10% of the variance was attributed to the four consensus genetic groups (Fig. S7). However, in the AMOVA focusing only on Mahogany and Squirrel Gliders, the variance explained between species became negative. Instead, nearly 75% of the genetic variation was explained by the outlier loci within the four genetic groups (Fig. S7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DAPC plot based on 101 outlier loci explained 82% of the total variance among the genetic groups (Fig. 4). PC1 (x-axis) had a high eigenvalue of 1908 and primarily differentiated CC Squirrel Gliders from Mahogany Glider and NQ Squirrel Gliders. PC2 (y-axis), with an eigenvalue of 691, further distinguished mid-eastern/south-eastern Queensland (SQ) Squirrel Gliders from the other three genetic groups. The plot also shows distinct genetic clustering of CC and SQ, while MG and NQ show significant overlap (Fig. 4).\u003c/p\u003e\n\u003ch4\u003eMorphological assessment among consensus genetic groups\u003c/h4\u003e\n\u003cp\u003eThe Mahogany Glider was originally described as a larger glider, with a relatively longer and more slender tail, compared to Squirrel Gliders. The measurements in this study generally confirmed these morphological differences (Fig. 5A). MG individuals were larger, with longer bodies and longer tails compared to NQ, CC, and SQ (Fig. 5A). However, they are not distinct for relative tail length, with the mean tail-to-body length ratio for NQ and CC being similar to that of Mahogany Gliders (Fig. 5A). Compared to the other groups, SQ have relatively shorter tails. However, larger sample sizes are required for NQ and CC.\u003c/p\u003e\n\u003cp\u003eThe Factor Analysis of Mixed Data (FAMD) revealed that individuals identified as Mahogany versus Squirrel Gliders could be distinguished by a combination of body length, tail length, head length, head width, and tail base thickness (slender versus wide/fluffy tail base); however, there was some overlap (Fig. 5B). The first dimension of the FAMD accounted for 36.5% of the variation, with body length, head length, and tail length contributing 26.25%, 26.15%, and 24.65%, respectively. Nevertheless, it is important to interpret the results cautiously due to different specimen types and limited numbers of individuals for NQ and CC.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.\u0026nbsp; \u0026nbsp;Conservation genetics of Mahogany Gliders\u003c/h3\u003e\n\u003ch4\u003eAfter confirming that Mahogany Gliders are a moderately distinct genetic and phenotypic group through the analyses above, we conducted a genetic assessment of this Endangered taxon.\u003c/h4\u003e\n\u003ch4\u003eGenetic structure analysis\u003c/h4\u003e\n\u003cp\u003eTwo distinct genetic clusters were identified within Mahogany Gliders. These clusters correspond to the sampling localities north of the Herbert River/Cardwell Range (Muller\u0026rsquo;s Creek, Cardwell, Murray Upper, and Tully) versus the sampling localities south of the Herbert River (Ollera Creek, Bambaroo, Allendale, and Easter Creek) (Fig. 1; Fig. 6). In the Discriminant Analysis of Principal Components (DAPC), K-means clustering identified two clusters as optimal, explaining 44% of total variation. The northern and southern clusters were separated by the first eigenvalue (811.8), while the second eigenvalue (106) showed some discrimination among localities within each of the northern and southern clusters (Fig. 6A). The Evanno plots generated during the STRUCTURE analysis supported the identification of two clusters (K) as optimal, aligning with the clustering observed in the DAPC analysis (Fig. S5). However, F\u003csub\u003eST\u003c/sub\u003e between the northern and southern clusters was relatively low (F\u003csub\u003eST\u003c/sub\u003e = 0.054, 95% CI: 0.051\u0026ndash;0.058), and the unsupervised membership grouping only identified one cluster within Mahogany Gliders.\u003c/p\u003e\n\u003cp\u003eThe northern cluster demonstrated greater genetic homogeneity, while the southern cluster showed more genetic substructure (Fig. 6B). Upon identifying the optimal two clusters (K = 2) in the STRUCTURE analysis, the southern cluster demonstrated genetic admixture from the northern cluster, though not reciprocally. At the second optimal clustering (K = 6), the northern cluster remained virtually homogenous, but the southern cluster showed more substructure. Interestingly, the Bambaroo site displayed its own genetic subcluster and had the least genetic admixture compared to other sampling localities in the southern cluster (Fig. 6B). NetView analyses yielded similar results \u0026mdash; while all Mahogany Gliders form a distinct cluster when the nearest neighbours were set to 20 (kNN = 20), at kNN = 10, three distinct groups emerged: \u0026nbsp;northern, southern, and Bambaroo groups (Fig. S3).\u003c/p\u003e\n\u003cp\u003eThe Mantel test results showed a statistically significant but weak isolation by distance, with only 12.6% (R\u003csup\u003e2\u003c/sup\u003e = 0.126) of the variation explained by geographical distance among the Mahogany Glider samples (Fig. S9A). Notably, after grouping the gliders into northern and southern clusters based on genetic structure analysis, genetic distances within the northern cluster exhibit a stronger correlation with geographical distance (R\u003csup\u003e2\u003c/sup\u003e = 0.236) (Fig. S9C).\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eComparative genetic diversity and effective population size estimates\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe neutral genetic diversity between Mahogany Gliders and Squirrel Gliders is similar. Mahogany Gliders exhibit a standardized multi-locus heterozygosity (sMLH) of 1.13, while Squirrel Gliders have a sMLH of 0.895. The observed heterozygosity (Ho) is 0.116 for Mahogany Gliders and 0.089 for Squirrel Glider, and the expected heterozygosity (He) is 0.13 for Mahogany Gliders and 0.118 for Squirrel Glider (Table S3). Note that the inbreeding coefficient (F\u003csub\u003eIS\u003c/sub\u003e) is higher in Squirrel Gliders in comparison to Mahogany Gliders, likely due to\u0026nbsp;the mixture of structured populations (Wahlund effect) (De Mee\u0026ucirc;s 2018).\u003c/p\u003e\n\u003cp\u003eWithin Mahogany Gliders (MG), the sMLH ranges from 0.78 to 1.06, with an average of 0.98. The H\u003csub\u003eo\u003c/sub\u003e varies from 0.14 to 0.19, averaging at 0.17, and the F\u003csub\u003eIS\u003c/sub\u003e ranged from 0.01 to 0.20, with an average of 0.07 (Table 3; Table S3). The northern cluster showed lower genetic diversity indices compared to the southern cluster, with individuals from Murray Upper\u0026mdash;the northernmost site of their current known range\u0026mdash;exhibiting the lowest genetic diversity (sMLH = 0.776, FIS = 0.201). Bambaroo, despite its small size and isolation, displayed a wide range of sMLH (0.75\u0026ndash;1.26) and H\u003csub\u003eo\u003c/sub\u003e (0.13\u0026ndash;0.22) values, with a low F\u003csub\u003eIS\u0026nbsp;\u003c/sub\u003eof 0.05 (Fig. S8; Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effective population size could only be estimated for the three sampling localities with more than six samples: Allendale, Bambaroo, and Easter Creek (Table 3; Table S4). In Allendale, the effective population size was low, with a mean of 37 individuals (parametric CI: 35.4\u0026ndash;38.8, Jackknife CI: 7.8\u0026ndash;infinite). Bambaroo also exhibited a low effective population size, with a mean of 27.6 individuals (parametric CI: 27.1\u0026ndash;28.1, Jackknife CI: 19\u0026ndash;45.1). Conversely, Easter Creek displayed a notably high effective population size, with a mean of 431.2 individuals (parametric CI: 324.4\u0026ndash;640.6, Jackknife CI: 45.8\u0026ndash;infinite).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Genetic diversity metrics and effective population size estimates for Mahogany Gliders, including south and north populations, and sampling localities. Metrics provided are the number of individuals (nInd), standardized multilocus heterozygosity (sMLH), observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (FIS), and effective population size estimates (Ne). Refer to Table S3 and S4 for a full table with standard deviations and confidence intervals that includes Squirrel Glider genetic groups.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"518\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003enInd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003esMLH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eHo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eFIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eNe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eAll-species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 88px;\"\u003e\n \u003cp\u003eMahogany Glider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eSouth MG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eNorth MG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eAllendale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eBambaroo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eCardwell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eEaster Ck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e431.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eMuller\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eMurray Upper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eOllera Ck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eTully\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion ","content":"\u003cp\u003eAiming to inform effective conservation management of the Endangered Mahogany Glider, we utilised genomic tools at both broad and fine scales. We firstly examined the population genetic structure of the Mahogany Glider (\u003cem\u003ePetaurus gracilis)\u003c/em\u003e, Squirrel Glider (\u003cem\u003eP. norfolcensis\u003c/em\u003e) and Krefft\u0026rsquo;s Glider (\u003cem\u003eP. notatus\u003c/em\u003e) to gain insight on their evolutionary relationships and genetic distinctiveness of the Mahogany Glider. Subsequently, we conducted a conservation genetic assessment for the Mahogany Glider. The analyses confirmed that the Krefft\u0026rsquo;s Glider is highly divergent from the other two species. In contrast, genetic relationships between Mahogany and Squirrel Gliders are more complex. Genetic structure and phylogenetic analyses consistently identified four genetic groups: Mahogany Gliders from the lowlands of the Wet Tropics, \u0026lsquo;Squirrel Gliders\u0026rsquo; from inland North Queensland (NQ), \u0026lsquo;Squirrel Gliders\u0026rsquo; from Cape Cleveland near Townsville (CC), and Squirrel Gliders from mid-eastern and south-eastern Queensland (i.e., from about Charters Towers and Proserpine south).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMahogany Gliders are generally distinct from the other three genetic groups in the genetic analyses but with genetic admixture evident with the NQ and CC genetic groups (\u0026shy;\u0026shy;\u0026shy;Fig. 2). The genetic admixture between Mahogany and Squirrel Gliders suggests that historical or contemporary introgression has occurred between these species. The complexity of relationships between Mahogany and Squirrel Gliders has been previously suggested in phylogenetic studies on the Petaurid gliders. For instance,\u0026nbsp;Malekian et al. (2010)\u0026nbsp;revealed a genetic difference of only 1.8\u0026ndash;2.2% between Mahogany and Squirrel Gliders for two mitochondrial genes (ND2 and ND4) and one nuclear marker (\u0026omega;-globin). A different phylogenetic study, based on ND2 mitochondrial gene and ApoB1 nuclear gene, clustered Mahogany Glider samples with Squirrel Glider samples from Hervey Range (west of Townsville) and Einasleigh Uplands (west of Atherton Tablelands)\u0026nbsp;(Ferraro 2012). The phylogenomic relationship we present here shows Mahogany Gliders nested within the broad Squirrel Glider clade but does show Mahogany Gliders as a highly supported, distinct clade (\u0026shy;\u0026shy;\u0026shy;\u0026shy;Fig. 3).\u003c/p\u003e\n\u003cp\u003eWe found substantial introgression between Mahogany Gliders and the gliders from North Queensland (NQ) (samples collected near Princess Hills, Atherton Tablelands, and Chillagoe) (Fig. 2C). The NQ gliders also group closely to Mahogany Gliders in signature of selection based on outlier loci and structure analyses based on neutral loci (e.g., NetView, Fig. 2A; DAPC, Fig. 2B; Signature of selection, Fig. 4). The low F\u003csub\u003eST\u003c/sub\u003e value between NQ gliders and Mahogany Gliders further support the close relationship between them (Table 2). Interestingly, in the SNPs-based phylogeny (Fig. 3\u0026shy;\u0026shy;\u0026shy;\u0026shy;), the NQ samples are divergent to a monophyletic group of Mahogany and Squirrel Gliders, rather than being clustered within it (\u0026shy;\u0026shy;\u0026shy;\u0026shy;Fig. 3). The morphology is interesting for the NQ gliders \u0026mdash; they are of similar body size to Squirrel Gliders, but their relative tail length is more akin to Mahogany Gliders (albeit based on a small sample size) (Fig. 5). Overall, the results suggest a close genetic relationship between NQ gliders and Mahogany Gliders, and whether hybridisation or cryptic (sub)species exist in NQ gliders requires further sampling and analyses (Malinsky \u003cem\u003eet al.\u003c/em\u003e 2018). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe gliders from Cape Cleveland (CC) are identified as a genetically distinct group in most of the analyses. They are more closely related to Squirrel Gliders but show signs of introgression from Mahogany Gliders (e.g., NetView, Fig. 2A; Structure, Fig. 2C; phylogeny, \u0026shy;\u0026shy;\u0026shy;\u0026shy;Fig. 3). In some analyses, they appear as a distinct group (e.g., DAPC, Fig. 2B; Pairwise F\u003csub\u003eST\u003c/sub\u003e, Table 2) and exhibit a relatively long and slender tail, similar to that of Mahogany Gliders. Additionally, CC samples exhibit a unique signature of selection that differs from both Mahogany and Squirrel Gliders (Fig. 4). It is likely that the gliders from Cape Cleveland are adapted to their local coastal habitat, a peninsula of tropical lowland eucalyptus woodlands, rainforest, and wetlands. The genetically and ecologically distinct CC gliders are therefore potentially qualified as a subspecies of Squirrel Glider with further sampling and analyses. Furthermore, we recorded a high glider density at Cape Cleveland, with a catch rate of 15.4%. This high density, along with their greater genetic diversity compared to other Squirrel Gliders (Table 3), indicates that the CC population is genetically healthy.\u003c/p\u003e\n\u003cp\u003eWhile genetic admixture exists between the Mahogany Glider and other genetic groups, compelling evidence supports the classification of the Mahogany Glider as at least a highly distinct subspecies. This classification finds support in their status as a monophyletic clade, distinct clustering, and characteristic morphology (e.g., phylogeny, \u0026shy;\u0026shy;\u0026shy;\u0026shy;Fig. 3; NetView, Fig. 2A; Morphology, Fig. 2C;), which suggest a distinct evolutionary path despite their close genetic relationship to the Squirrel Gliders (\u0026shy;\u0026shy;\u0026shy;\u0026shy;Fig. 3). Morphological features are also unique in Mahogany Gliders, as seen in the FAMD analysis (Fig. 5). These morphological distinctions are consistent with the species description of the Mahogany Glider by Van Dyck (1993) and measurements presented elsewhere (e.g., Ferraro 2012). Furthermore, as for CC gliders, signature of selection analysis suggests potential local adaptation in the Mahogany Glider (Fig. 4). These findings indicate that the Mahogany Glider has potentially adapted in a unique way to the lowland open forests of the Wet Tropics. Further research is needed to fully understand the factors driving local adaptation and its implications for the conservation of this species.\u003c/p\u003e\n\u003ch3\u003eConservation genetics of Mahogany Gliders\u003c/h3\u003e\n\u003cp\u003eGenetic diversity of the Mahogany Glider is generally comparable to the non-threatened Squirrel Gliders analysed in this study. Comparisons of heterozygosity based on SNPs, employing similar filtering methods, indicate that the observed heterozygosity (H\u003csub\u003eo\u003c/sub\u003e) of the Mahogany Glider is comparable to other threatened marsupials listed in the EPBC Act 1999, such as the Koala, Northern Bettong, Western Barred Bandicoot and Greater Glider (Table 3). However, this range of observed heterozygosity is lower when compared to species with a vulnerable status, such the Greater Bilby, Burrowing Bettong, Long-nosed Potoroo, and Golden Bandicoot (Table 4). Caution should be taken when comparing genetic indices across species, as these indices are heavily dependent on the evolutionary history and population genetics of each species and thus can be influenced by biases introduced through different filters, thresholds, and sample sizes (Schmidt et al. 2021). For instance, despite being categorized as Least Concern in terms of conservation status, the Sugar Glider (\u003cem\u003eP. breviceps\u003c/em\u003e) and Krefft\u0026rsquo;s Glider consistently exhibit low observed heterozygosity (Knipler \u003cem\u003eet al.\u003c/em\u003e 2022), as also seen for Krefft\u0026rsquo;s Gliders in our study here (Table S3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Observed SNPs heterozygosity (H\u003csub\u003eo\u003c/sub\u003e) ranges of selected Australian marsupials with EPBC conservation status.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.109%;\"\u003e\n \u003cp\u003eEPBC Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7115%;\"\u003e\n \u003cp\u003eH\u003csub\u003eo\u003c/sub\u003e (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.8462%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eMahogany Glider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eEndangered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e0.12 (0.16\u0026ndash;0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eKoala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eEndangered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e(0.22\u0026ndash;0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eKjeldsen et al. 2019\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eNorthern Bettong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eEndangered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e(0.15\u0026ndash;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003eTodd et al. 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eWestern Barred Bandicoot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eEndangered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e(0.14\u0026ndash;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eWhite et al. 2018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eGreater Glider (south)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eEndangered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e0.14 (0.09\u0026ndash;0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eKnipler et al. 2023\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eGreater Bilby\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eVulnerable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eWhite et al. 2018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eBurrowing Bettong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eVulnerable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e(0.18\u0026ndash;0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eWhite et al. 2018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eLong-nosed potoroo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eVulnerable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eMulvena et al. 2020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eGolden Bandicoot (mainland)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eVulnerable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e(0.28\u0026ndash;0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eRick et al. 2023\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eRed-tailed Phascogale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eVulnerable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e(0.19\u0026ndash;0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003ePierson et al. 2023\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSquirrel Glider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eLeast Concern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eKnipler et al. 2021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSugar Glider (\u003cem\u003eP. breviceips\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.109%;\"\u003e\n \u003cp\u003eLeast Concern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.7115%;\"\u003e\n \u003cp\u003e(0.15\u0026ndash;0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.8462%;\"\u003e\n \u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-AU\"\u003eKnipler et al. 2022\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eStructure analyses have revealed two distinct genetic clusters within Mahogany Gliders \u0026mdash; the northern and the southern cluster (Fig. 6). The Cardwell Range, situated between these two clusters, appears to serve as a natural barrier. Interestingly, despite this geographical division, genetic admixture persists between the northern and southern groups. This admixture could be a result of natural gene flow through the upper catchment of the Herbert River, or movements across the Herbert River (which is narrow in places). Interestingly, there is a significant northern genetic component in the southern cluster, and this asymmetry cannot be explained based on data to date.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe northern cluster is characterized by lower genetic diversity comparing to the southern cluster, a more homogeneous genetic structure (Fig. 8B), and moderate isolation by distance (24% of genetic variation in this cluster is explained by isolation by distance; Fig. S9). Acceptable connectivity of populations may exist through this area, but perhaps only until recently. \u0026nbsp;The catch rate at Muller\u0026rsquo;s Creek was previously recorded between 7.5\u0026ndash;15% in 1995\u0026ndash;1996 (Jackson 1998) and around 11.5% in 2008 (personal communication with Mark Parsons, Queensland Government 2020). These high catch rates suggest high glider density in a quality habitat. In contrast, recent catch rate at Muller\u0026rsquo;s Creek was lower in our study using similar field techniques \u0026mdash; just 2% and 3% in 2021 and 2022, respectively (Table S1). Furthermore, individuals from Murray Upper, the northernmost known population of the species, exhibit worryingly low individual heterozygosity (H\u003csub\u003eo\u003c/sub\u003e and sMLH; Table 3), indicating the population is inbred to some degree. The massive clearance and sugar cane farming in the Tully region from 1880 to 1905 removed and fragmented much suitable habitat (QImaginary 1957; Bolton 1970; The University of Queensland 2018), a situation exacerbated by further clearing over the following decades and widespread habitat damage during Cyclone Yasi (Holloway 2013). However, ongoing forest thickening due to changes in fire regimes (Stanton et al. 2014a; Stanton et al. 2014b) may be a key issue in reducing habitat suitability in recent times in the northern half of the range (Chang \u003cem\u003eet al.\u003c/em\u003e 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe southern cluster of the Mahogany Glider is genetically more structured across different sampling localities (Fig. 6), and only 10% of genetic variation is explained by isolation by distance (Fig. S9). Therefore, factors other than distance are likely contributing to the population structure. The most likely explanation is habitat fragmentation and hence restricted movement between populations. However, catch rates at some southern sites were high, suggesting considerable abundance within the habitat fragments. Catch rates were high at Bambaroo, Allendale and Easter Creek (Table S1), and these sites had higher genetic diversity than sites in the northern cluster (Table 3). These sites are intriguing given how small these fragments are and low Ne estimates for Bambaroo (Ne = 27.6) and Allendale (Ne = 64.7). This discrepancy may reflect a delay in the loss of neutral genetic diversity because neutral genetic diversity lags behind population isolation and decline (Pinto \u003cem\u003eet al.\u003c/em\u003e 2023). The connectivity between Bambaroo and adjacent coastal forest was lost in 1988 (QImaginary 1951; Google Earth imagery through time) and Easter Creek only became fragmented during extensive logging from 1987 to 1997 (QImaginary 1993; Google Earth imagery through time). Given population isolation only occurred in the past 40 years, the full impact of habitat loss and fragmentation on the density and genetic diversity of the Mahogany Gliders in this area is yet to be seen.\u003c/p\u003e"},{"header":"Conclusion and Management Recommendations","content":"\u003cp\u003eThis study has shown that the genetics of Mahogany and Squirrel Gliders is complex in Queensland, but that four consensus genetic groups are supported: Mahogany Glider, North Queensland gliders, Cape Cleveland gliders and Squirrel Gliders from mid-eastern/south-eastern Queensland. Evidence of genetic introgression between these groups was found, but whether it is historical or ongoing remains unclear. A first step for conservation is resolving the taxonomic uncertainty associated with these four genetic groups. It is possible that all four groups represent subspecies of the Squirrel Glider, but this requires further genetic and morphological investigation. Sampling should focus on the broad areas of introgression and narrow in on areas of potential contact zones of the four genetic groups. Genetic and phenotypic investigations at these contact zones could resolve the current level of genetic isolation \u0026nbsp;(e.g., Hoskin et al. 2005; Harrison and Larson 2016; Malinsky et al. 2018; Caeiro-Dias et al. 2021) and help resolve the taxonomy.\u0026nbsp;Key sampling areas are located at the southern and western edge of the Mahogany Glider distribution (for contact with NQ and CC), and the Charters Towers\u0026ndash;Townsville\u0026ndash;Ayr region (for contact between NQ, SQ and CC).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn regard to the ongoing conservation of the Mahogany Glider, the genetic and morphological data presented here suggest that this taxon is at least a subspecies, that is, either as a full species or a subspecies of the Squirrel Glider complex. In Australia, subspecies receives the same conservation status as species under the EPBC Act 1999 (Threatened Species Scientific Committee 2023). Given the ongoing threats of habitat loss for the Mahogany Glider, it is imperative that current conservation efforts continue to ensure the long-term survival of these genetically and morphologically distinct gliders. The next critical conservation step involves monitoring population trends and developing tailored conservation strategies for the northern and southern genetic clusters. These strategies should aim to increase population sizes and genetic diversity in the northern cluster and increase connectivity and effective population sizes in the fragmented southern cluster. The latter can be achieved through a combination of assisted gene flow and revegetation of functional habitat corridors. Continuous genetic monitoring of vulnerable populations, particularly those with low effective population sizes and poor genetic diversity, is essential. Additional surveys should continue to better resolve the fine-scale distribution and local densities of the Mahogany Glider, to better understand habitat determinants, connectivity, and target future conservation genetic research.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe acknowledge the traditional owners of the lands where the fieldwork was conducted: Nywaigi (south of Ingham), Warrgamay (Ingham region), Girringun (Cardwell and Ingham regions), Girramay (Cardwell region), and Gulngay (Tully region). We thank Jacqui Diggins and Terrain Natural Resource Management for the support throughout this project. We thank Jessica Worthington Wilmer and Heather Janetzki from the Queensland Museum for assistance with accessing genetic samples and specimens for measurement, respectively. We are thankful to Daryl Dickson and Mark Parsons for providing additional genetic samples of Mahogany Gliders. Funding for the research was generously provided by Terrain NRM, Holsworth Wildlife Research Endowment, and James Cook University. The dedication and hard work of the many volunteers who assisted with the fieldwork were instrumental to the success of this study, especially the contribution from Chieh Lin and Jonathan Ronnle. We appreciate the expert advice on fieldwork and field site planning provided by Steve Jackson, Nicole Prajbisz, Mark Parsons, Alex Tessieri, and Chris Muriata. The support during fieldwork from rangers of Queensland Parks and Wildlife Services was invaluable, with a particular thanks to Tim Devlin, Joshua Spina, and William White. We thank Megan Higgie, Nicholas Bail, Jordy Groffen (James Cook University), and Paul Ferraro (DEECA) for advice and comments on the results and the manuscript. We thank Marine Lechene for the illustration of the gliders in this manuscript. We acknowledge the communication and support from members of Mahogany Glider Recovery Team and are grateful to the property owners for granting permission to access their properties for the purpose of this study. Lastly, we would like to express our gratitude to Veronica Green for her generous donation of 5 kg of Hinchinbrook Honey and Golden Circle company (Joel Roberts) for donating 80 bottles of raspberry cordial for the surveys.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Terrain Natural Resource Management, Holsworth Wildlife Research Endowment, and College of Science and Engineering of James Cook University.\u003c/p\u003e\n\u003ch2\u003ePermits and Animal Ethics\u003c/h2\u003e\n\u003cp\u003eThe research was conducted in accordance with Queensland Department of Environment and Permits for Scientific Research (protected areas: P-PTUKI-100021853; non-protected areas: WA0025939) and James Cook University animal ethics (A2699).\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eConrad Hoskin and Yiyin Chang contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yiyin Chang. The first draft of the manuscript was written by Yiyin Chang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in FigShare, DOI TO BE PROVIDED.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdamack AT, Gruber B (2014). PopGenReport: Simplifying basic population genetic analyses in R. Methods in Ecology and Evolution 5, 384\u0026ndash;387. doi:10.1111/2041-210X.12158\u003c/li\u003e\n\u003cli\u003eAustralian Government (2020). 20 mammals by 2020. Department of Agriculture, Water and the Environment. Available at: https://www.environment.gov.au/biodiversity/threatened/species/20-mammals-by-2020 [accessed 25 April 2020]\u003c/li\u003e\n\u003cli\u003eBender DJ, Contreras TA, Fahrig L (1998). Habitat loss and population decline: a meta‐analysis of the patch size effect. Ecology 79, 517\u0026ndash;533. doi:10.1890/0012-9658(1998)079[0517]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eBertola L V., Higgie M, Zenger KR, Hoskin CJ (2023). Conservation genomics reveals fine-scale population structuring and recent declines in the Critically Endangered Australian Kuranda Treefrog. Conservation Genetics 24, 249\u0026ndash;264. doi:10.1007/s10592-022-01499-7\u003c/li\u003e\n\u003cli\u003eBolton GC (1970). \u0026lsquo;A thousand miles away: a history of North Queensland to 1920\u0026rsquo;. (Australian National University Press)\u003c/li\u003e\n\u003cli\u003eBurbidge A, Woinarski J, Harrison P (2014). \u0026lsquo;Action Plan for Australian Mammals 2012\u0026rsquo;. 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Bioinformatics 28, 3326\u0026ndash;3328. doi:10.1093/bioinformatics/bts606\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Habitat Fragmentation, Petaurus gliders, Threatened Species, Genomics, SNPs, Genetic Structure, Genetic Diversity, Taxonomic Uncertainty","lastPublishedDoi":"10.21203/rs.3.rs-5041688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5041688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Squirrel Gliders (Petaurus norfolcensis) are widely distributed throughout the woodlands of eastern Australia, while the similar but larger Mahogany Glider (Petaurus gracilis) inhabits the coastal woodlands of the Wet Tropics in northeastern Queensland. The Mahogany Glider is an Endangered species due to habitat loss and fragmentation. To inform effective conservation management, this study used single nucleotide polymorphism markers (SNPs) from field and museum-derived samples to investigate genetic relationships within the Squirrel/Mahogany Glider complex and conduct a conservation genetics assessment for the Mahogany Glider. Analyses of genetic structure, phylogenomics, and outlier loci identified four genetic groups: Mahogany Glider and three distinct groups in Squirrel Gliders (North Queensland, Cape Cleveland, and mid-eastern/south-eastern Queensland). We found genetic admixture between these groups, but whether the admixture is historic or current remains unclear. Gliders from North Queensland were genetically more similar to Mahogany Gliders in some analyses than to the other Squirrel Glider groups. Morphological analysis confirmed that Mahogany Gliders are distinguishable from other gliders by their larger body size and longer tail. The study emphasizes the taxonomic uncertainty in the Mahogany-Squirrel Glider complex and the need to investigate glider populations at contact zones. When assessing Mahogany Gliders alone, we found a clear north-south split in genetic structuring, with the southern cluster being more structured than the northern cluster. Genetic diversity within Mahogany Gliders was generally comparable to that of Squirrel Gliders, but some sampling localities indicated loss of genetic diversity and low effective population size. Regardless of whether Mahogany Gliders are classified as a species or subspecies, their Endangered status underscores the need for targeted conservation efforts. The genetic findings offer practical pathways for on-ground management to enhance population recovery and connectivity.","manuscriptTitle":"Conservation genetics of Mahogany Gliders and insights into their evolutionary relationship with Squirrel Gliders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 05:58:29","doi":"10.21203/rs.3.rs-5041688/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-03T08:18:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-15T09:23:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-26T05:54:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220742526965245540616745631275123007122","date":"2024-09-24T14:16:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22597153996562980893832116234286440576","date":"2024-09-23T07:56:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-18T12:55:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-06T15:02:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-06T15:02:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2024-09-06T05:22:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"61c82adc-08b3-47dd-9919-b6e8de2cc8b2","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T15:58:55+00:00","versionOfRecord":{"articleIdentity":"rs-5041688","link":"https://doi.org/10.1007/s10592-025-01700-7","journal":{"identity":"conservation-genetics","isVorOnly":false,"title":"Conservation Genetics"},"publishedOn":"2025-04-30 15:57:06","publishedOnDateReadable":"April 30th, 2025"},"versionCreatedAt":"2024-10-10 05:58:29","video":"","vorDoi":"10.1007/s10592-025-01700-7","vorDoiUrl":"https://doi.org/10.1007/s10592-025-01700-7","workflowStages":[]},"version":"v1","identity":"rs-5041688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5041688","identity":"rs-5041688","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}