Last Glacial and Holocene dynamics override post-colonial disturbance in shaping genetic diversity of a heavily exploited palaeoendemic conifer, Lagarostrobos franklinii

Last Glacial and Holocene dynamics override post-colonial disturbance in shaping genetic diversity of a heavily exploited palaeoendemic conifer, Lagarostrobos franklinii | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Last Glacial and Holocene dynamics override post-colonial disturbance in shaping genetic diversity of a heavily exploited palaeoendemic conifer, Lagarostrobos franklinii James Worth, James Marthick, Yoshihisa Suyama, Gregory Jordan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6792156/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Heredity → Version 1 posted 8 You are reading this latest preprint version Abstract The impact of past anthropogenic disturbance on the amount and distribution of genetic diversity is a key factor in determining the resilience of tree species to environmental change. This is particularly the case for narrowly distributed species where this disturbance has impacted most of the species’ range. Here we examine the legacy of post-colonial logging and fire on patterns of genetic diversity in the Tasmanian palaeoendemic conifer Lagarostrobos franklinii (Podocarpaceae), a fire sensitive and slow growing rainforest tree valued for its durable timber. Thirty-three populations (12 of which represent primary stands) from across the species range were genotyped using 8 nuclear SSRs (871 samples) and MIG-seq-based single nucleotide polymorphisms (254 samples). Genetic differentiation was relatively high for conifers (Fst of 0.113 and 0.143 for nuclear SSR and MIG-seq, respectively) with the most diverged populations near the species northern and southern range limits and cryptic divergence between populations geographically close but in differing river catchments likely reflecting postglacial dispersal from distinct Last Glacial refugia and low levels of gene flow. Population level genetic diversity was greatest in the core of the range with no significant correlation with the history of post-colonial human disturbance (i.e. primary vs. non primary stands) and, unexpectedly, given the greater impact of logging at lower elevations, a significant decline in allelic richness with elevation. Overall, this study shows that L. franklinii has been resilient to past timber exploitation and uncovers previously undetected genetic patterns that will help guide the conservation of this important conifer into the future. Biological sciences/Genetics Biological sciences/Evolution/Population genetics Biological sciences/Ecology/Conservation biology conservation priority core vs peripheral populations cryptic genetic divergence human impact MIG-seq nuclear SSRs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Most of the world’s forests have been impacted by agriculture, timber extraction and fire incursion, with only around 30% of the world’s forests considered to be primary and even lower percentages remaining in the northern hemisphere temperate zone (Parviainen, 2005; Pelz et al. , 2023). Human disturbance has mostly had negative impacts on tree species genetic diversity (Ledig, 1992), with potential deleterious implications for these species’ capacity to tolerate and/ or adapt to environmental change (Schaberg et al. , 2008). In recognition of the need to protect forests and their intrinsic biodiversity and ecosystem services, the area of conserved forests has risen across the globe since the 1990s (Morales-Hidalgo et al. , 2015). However, while direct disturbance by humans including timber extraction has largely ceased in these protected areas, the legacy of past human impacts on the amount and distribution of genetic diversity in forest tree species is poorly understood for most species. The cool temperate island of Tasmania is the most forested state in Australia and retains 73.1% of its original native vegetation cover (Tasmanian Planning Commission, 2024). The western half of the island includes one of the few intact forest landscapes designated in southeastern Australia (Potapov et al. , 2008), with extensive areas of intact native vegetation comprised of a mosaic of moorland, eucalyptus forests and cool temperate rainforests. However, during the early 19th to mid-20th centuries, cool temperate rainforests were selectively logged by post-colonial Europeans for the decay resistant, high-quality timber of Tasmanian endemic Podocarpaceae and Cupressaceae conifers. The extensive river systems of the region allowed access to conifer stands across their ranges and also a means of transporting logs downstream. The chief species targeted for logging was Lagarostrobos franklinii (Hook. f.) Quinn (Podocarpaceae), or Huon pine, a slow growing, evergreen conifer distributed in western and southern Tasmania (Figure 1). It primarily occurs as a dominant tree of cool temperate rainforests along lowland rivers and the shores of sheltered sea inlets where it reaches heights of 10 – 38 m (Eckenwalder, 2009) but also occurs at higher elevations near lakes and streams in the upper watershed of catchments. A few stands not associated with water bodies, including some near the subalpine zone (Gibson et al. , 1991), are found in long unburnt areas (fire refugia) (Gibson, 1988). Of the estimated 10,500 ha of pre-colonial L. franklinii forest, 15% has been lost to dam construction and fire (Farjon and Page, 1999) while only approximately 10% remains primary (Kerr and McDermott, 1999). Selective logging occurred across the species’ range including in the core of its range, the rivers draining into Macquarie Harbour (Millington et al. , 1979), and in other important occurrences along the Huon and Davey Rivers in the south and the Pieman catchment at the northern edge of its range. Logging of L. franklinii declined as accessible trees were depleted, and concerns about the sustainability of the practice led to a ban in cutting green wood in the 1970s (Holbeck, 2022). Apart from the direct loss of trees, past logging may have resulted in an increase in burning of the landscape (Adeleye et al. , 2023) due to the use of fire to open up the dense vegetation to ease land access to L. franklinii stands. With no capacity to recover from fire L. franklinii is likely to have been impacted with the complete loss of stands documented in some areas (Peterson, 1990; Gibson et al. , 1991; Buckley et al. , 1997) although its mostly riverine habitat has resulted in fewer losses than for non-riverine fire-sensitive Tasmanian conifers such as Athrotaxis spp. (Holz et al. , 2015, 2020). Most of the range of L. franklinii is now protected in national parks. Given the species slow growth, fire sensitivity and high proportion of the species range that was selectively logged, impacts on genetic diversity and structure of L. franklinii forests may have been severe. However, this species remains an important part of the rainforest canopy and whether a legacy of post-colonial human impacts is evident at the genetic level is unknown. In other forest trees, including conifers, genetic analyses have found that primary forests can harbour a greater diversity of rare alleles, which are crucial in determining species adaptive capacity (Petit et al. , 1998), than logged forests (Mosseler et al. , 2003; Widiyatno et al. , 2016; Roque et al. , 2023). This loss of rare alleles is due to fragmentation of stands and reduction in density of individuals reducing effective population size and, in the case of selective logging, the loss of older age classes. In Tasmania, range-wide genetic studies of fire-sensitive palaeoendemic conifers, Athrotaxis cupressoides and Diselma archeri , found significant reductions in stand-level genetic diversity with increasing past fire severity (Worth et al. , 2017). This study investigates the genetic diversity, and its spatial distribution, within and between 33 stands of L. franklinii from across the species’ range. Twenty-one of the stands are recorded as having been selectively logged while 12 have remained untouched by past fires and selective logging based on Peterson (1990) and Kerr and McDermott (1999). Two genetic markers with contrasting mutational mechanisms were used: nuclear simple sequence repeats (SSRs) (Marthick et al. , 2020) and genome wide single nucleotide polymorphisms (SNPs) obtained using multiplexed ISSR genotyping by sequencing (MIG-seq) (Suyama and Matsuki, 2015). Here we aim to examine the impact of post-colonial human disturbance on L. franklinii including past logging on the species by comparing genetic diversity of: (1) primary stands untouched by past logging and fire versus non-primary stands; (2) stands at low elevation areas, i.e. more readily accessible to loggers, versus stands at less accessible higher elevations; and (3) more accessible riverine stands versus non-riverine ones. In addition, we assess whether past human disturbance may have differentially impacted the species genetic diversity across the major catchments containing the species. Lastly, we aim to identify populations whose loss would have the greatest impact on the species overall genetic diversity. Protecting remaining stands has become an increasing priority for L. franklinii because the frequency and severity of lightning ignited fires (Kirkpatrick et al. , 2018; Fletcher et al. , 2024) have increased within the distribution of this highly fire-sensitive species. Materials and Methods The species Lagarostrobos franklinii is a wind-pollinated, mostly dioecious conifer (Shapcott, 1993). Its seed are dispersed poorly by gravity, but may be dispersed over longer distances via water (Shapcott, 1997). The species is notable for being one of the longest lived trees in the world with individual stems reaching ages of more than 2500 years (Buckley, 1997) with clonal reproduction extending the lifespan of individual genets to potentially tens of millennia (Anker et al. , 2001). Lagarostrobos is an ancient Podocarpaceae genus (Biffin et al. , 2011) that was widespread across the southern hemisphere in the Cretaceous and Palaeogene periods (Brown et al. , 2021). The closest extant relative is the New Zealand endemic Manoao colensoi (Hook. f.) Molloy, a monotypic genus erected from Lagarostrobos (Molloy 1995), with the two genera diverging 64 mya to 84 mya (Biffin et al. , 2011; Khan et al. , 2023). The earliest definitive occurrence of fossils of the extant species is from the Early Pleistocene in western Tasmania (Hill and MacPhail, 1985). Sampling A total of 890 samples of L. franklinii were collected from 33 populations (an average of 26.4 samples per population) spanning most of the species’ range with the exception of the Spero/ Wanderer River area (Figure 1). The 33 populations represented the elevational range of the species, with the exception of the highest elevation stand at Mt Read which has been shown to consist of 4 clonal genotypes (Marthick et al. , submitted) and is not considered in this study. Adult trees were collected at least 20 m apart to avoid sampling closely related individuals. Laboratory work and scoring DNA was extracted using the DNeasy 96 PlantKit (Qiagen) and eight dinucleotide repeat microsatellite loci used to genotype all 890 samples. These included six loci from (Marthick et al. , 2020) (Huon_23318, Huon_50967, Huon_23863, Huon_18442, Huon_3945 and Huon_13112) and two developed for this study (Huon_7288: Forward primer 5` TTGCTTTGATTCTATTTGACGC–3` and R primer 5` AAGGCTTTTCTGGAACACGA–3` and Huon_50224: Forward primer 5` CATGTGCACCTAGCCAGAGA–3` and R primer 5`CCTCCCTCCAACATAGCAGA–3`). The PCR thermocycle followed Marthick et al. (2020). The length of the amplified products was analysed using an ABI 3130xl Genetic Analyzer and fragments scored using the Microsatellite Plugin in Geneious 9.1.5 (https://www.geneious.com). A subset of samples (254 samples from the 33 populations– an average of 7.7 samples per population) representing the within-stand spatial extent of samples used for the nSSR analyses and excluding samples identified as clonal in the nSSR dataset were selected for genotyping using MIG-seq (Suyama and Matsuki, 2015). The MIG-seq library was prepared using the protocol of Suyama et al. , (2022) with a two-step PCR amplification process, after which all the amplicons larger than 250 bp were purified and sequenced on an Illumina MiSeq platform using a MiSeq Reagent Kit v3 (150 cycles, Illumina, San Diego, CA, USA). Trimmomatic 0.39 was used to eliminate low-quality and extremely short reads that contained adapter sequences (Bolger et al. , 2014) after the first 17 bases of reads 1 and 2 (SSR primer regions and anchors) were skipped using "DarkCycle". De novo assembly was performed from the 53,467 – 304, 541 MIG-seq reads using Stacks 2.4 pipeline software (Catchen et al. , 2013; Rochette et al. , 2019) with default parameters. Next, loci filtering in the Stacks POPULATIONS program was undertaken as follows: the minimum percentage of shared loci to detect SNPs was set to 90%, the minimum number of minor alleles was set to 2, and the maximum allowable observed value of heterozygosity was set to 60%. The resulting 983 SNPs data set was then filtered to exclude loci with linkage disequilibrium coefficients below 0.6 in PLINK ver. 1.90 (Purcell et al. , 2007), producing a final dataset consisting of 845 SNPs. Data integrity For all eight nuclear SSR loci, allelic correlations among loci (indicating linkage disequilibrium) were tested at the population level in Genepop v 4.2 (Raymond and Rousset, 1995) with sequential Bonferroni correction (p < 0.05) (Holm, 1979). In addition, FreeNA (Chapuis and Estoup, 2007) was used to test the potential for null alleles impacting estimates of genetic diversity and differentiation. This program estimates the null allele frequency at each locus and also calculates global Fst with and without null allele correction (Chapuis and Estoup, 2007). To detect if any nuclear SSR and MIG-seq SNP loci were potentially subject to selection the Fst outlier method, BayeScan v2.1 (Foll and Gaggiotti, 2008), was used with each run repeated three times. Existing genetic structuring can increase the number of false positives (Excoffier et al. , 2009), therefore, the analyses were undertaken using genetic clusters detected in Bayesian Analysis of Population Structure (BAPS) version 6 (Corander et al. , 2003) implementing ‘clustering of groups of individuals’ with a maximum K of 20. BAPS identified 13 clusters with probability of 0.79 for the nSSR dataset and 3 clusters for the MIG-seq SNP data set with a probability of 1 (Figure S3). The false discovery rate of BayeScan can be impacted by demographic history (e.g. island model, isolation by distance or expansion from refugia), therefore, we tested more realistic prior odds of 100 and 1,000 following (Lotterhos and Whitlock, 2014) and assessed each locus under Jeffreys’ scale of evidence (Jeffreys, 1998). Genetic diversity and structure analyses For the nuclear SSR dataset the extent of clonality was investigated in GenAlEx 6.5 (Peakall and Smouse, 2006) using the ‘find clones’ function. If clonality was detected, subsequent analyses included only one individual from each clone. Genetic diversity indices ( Na , Ne , Ho , He , uHe and percent of polymorphic loci) were analysed in GenAlEx 6.5 at the species, population and locus level for both nuclear SSR and MIG-seq SNP datasets. Rarefied allelic richness ( Ar ), which is strongly influenced by the abundance of rare alleles (El Mousadik and Petit, 1996), and private allelic richness ( PAr ) were calculated using a samples size of the smallest population minus one in HP Rare 1.1 (Kalinowski, 2005) for the nSSR dataset and Metapop2 v2.4.2 (López‐Cortegano et al. , 2019) for the MIG-seq dataset. Three measures of genetic differentiation, genetic differentiation among populations ( Fst ), Hedrick's standardized ( G'stH ) (Hedrick, 2005) and Jost’s D (Jost, 2008) were calculated in GenAlEx 6.5 and the mmod package (Winter, 2012) in R (R Core Team, 2022). Neighbour joining trees were constructed in SplitsTree 4.14.4 (Huson and Bryant, 2006) using a matrix of Nei’s D A (Nei et al. , 1983) calculated in Populations 1.2.32 (Langella, 2011) for the nuclear SSR dataset and a matrix of Nei’s distance calculated in the R program adegenet (Jombart, 2008) for the SNP dataset. For both datasets, isolation by distance (IBD) was tested for using the mantel.randtest command in adegenet package (Jombart, 2008) in R using 9999 permutations. For this purpose, decimal coordinates were converted to geodesic distances in pegas (Paradis, 2010) and a distance matrix calculated using Nei’s distance in adegenet. In addition, correlation between elevation and Ar were assessed in Jamovi 2.6.17 (The jamovi project, 2025) using the Pearson correlation coefficient for both datasets. One-way analysis of variance (ANOVA) undertaken in Jamovi 2.6.17 was used to determine if there were statistically significant differences between the means of genetic diversity indices ( Ne , Na , Ho , He , uHe , Ar , Par and Fis ) grouped according to catchment, stand type (riverine/creek, lake and not associated with water body) and human disturbance history (disturbed, primary). Here we treat the Franklin River and rivers/streams that feed it, a large tributary of the Gordon River, as a separate catchment because of the genetic distinctiveness of populations there (see Results). Population prioritization for conservation The contribution of each population to the overall species level allelic diversity, including its within- and between-population components, were calculated for both the nuclear microsatellite and MIG-seq datasets using Metapop2 v2.4.2 (López‐Cortegano et al. , 2019). This program assesses the contribution of each population to overall species-level allelic diversity by assessing the impact of the removal of a given population on the global allelic diversity of the remaining populations (as a percentage of change in diversity). Results Data integrity of nuclear SSR loci Nineteen individuals were excluded to avoid analysing ramets of the same clone as detected by GenAlEx. Clonal individuals were found in 14 populations with between 1-3 individuals removed from those populations (Table 1). In total, 3 of the 924 population by locus-pairs were found to have significant allelic associations (results not shown) indicating there were no consistent linkage disequilibrium between loci across populations. The estimates of null allele frequency were small, averaging 0.016 across all loci (between 0.007 to 0.028) (Table S1). Null alleles had negligible effect on overall genetic differentiation with global Fst including null alleles 0.100 compared to 0.099 when excluding null alleles (Table S1). Posterior probabilities were low for all nuclear SSR loci with an average across the three runs of 0.014 under prior odds of 100 and 0.00072 under prior odds of 1000 (Table S2) suggesting no evidence for either balancing or diversifying selection. Similarly, for MIG-seq loci posterior probabilities were low for all loci with an average of 0.0098 across the three runs under prior odds of 100 and 0.00099 under prior odds of 1000 (Table S3). Nuclear SSR genetic diversity and structure For the nuclear SSR dataset, a total of 80 alleles were observed with the number of alleles per locus ranging from 4 to 18 (Table S4). The amount of missing data was 0.13%. The mean number of different alleles ( Na ) and the number of effective alleles averaged over all loci ( Ne ) was 4.08 and 2.12, respectively (Table S4). Observed heterozygosity ( Ho ) and expected heterozygosity ( He ) across loci ranged between 0.38-0.62 (average = 0.49) and 0.36-0.61 (average = 0.48), respectively (Table S4). The inbreeding coefficient ( Fis ) values for each locus were all negative, varying between -0.05 to -0.01, except for Huon_7288 (0.01). Fst , G'stH and Jost’s D values across loci ranged between 0.08-0.14, 0.11-0.29 and 0.06-0.21, respectively (Table S4). Genetic diversity was distributed unevenly across the species range with highest Ho , He and Ar in the King, Bird, Franklin and Gordon/ Denison catchments (Figure 2 and Table 2). All but two populations with Ar above the overall population-level average were from these catchments, with highest Ar on the Franklin River at Kutikina Cave, Denison River and Jane River (Figure 2, Table S5). Lowest Ar values were observed in the northern and southern extremities of the range in the Pieman and Huon catchments including Yellow Creek, Stanley River and Lake Judd. However, some populations in these catchments had notably high genetic diversity including the Harman River (highest Ho ) and above average Ar for Corrina and Southwood (Figure 2, Table S5). Private alleles were found in 12 populations across all catchments with a maximum of 2 per population. Eight populations had one private allele while 2 were observed at Corrina, Newell Creek, Lake Marilyn and Denison River (Figure 3). Geographic structure was moderate with an average Fst of 0.113, GstH of 0.190 and Jost’s D of 0.105. Generally, geographically close populations were more closely related especially in the isolated Pieman and Davey catchments and the Franklin and King/ Bird catchments which formed distinct clusters in the neighbour joining (NJ) tree (Figure 4). On the other hand, populations from the Huon catchment were not all grouped together in the NJ tree with some closer to populations in the Gordon/ Denison catchment in the core of the species range. Genetic differentiation of populations was significantly associated with geographic distance ( r = 0.2156, p = 0.0186 - Mantel test, Figure S1). A significant correlation between elevation (m asl) and Ar was evident ( r = -0.391, P = 0.024) due to a decline in Ar with elevation (Figure 5). Apart from Na , Ar , PAr and Fis , all genetic diversity indices were higher on average for disturbed stands compared to primary stands (Table S7) but no differences were statistically significant (Figure 6 and Table S8). Similarly, for stand type no differences were statistically significant (Table S9-S10). In contrast, statistically significant differences ( P < 0.05) were observed between catchment for mean Ar and He (Table S11-S12). MIG-seq genetic diversity and structure The overall amount of missing data was 4.03%. The genetic diversity at MIG-seq SNP loci was similar to the nSSR dataset with both Ar and Ho population-level values significantly correlated using the Pearson correlation coefficient (for Ar r = 0.686 and P = 0.00001 and for Ho r = 0.374 and P = 0.0316). Highest Ho , He and Ar were observed in the Bird, Franklin, Gordon/ Denison catchments and, unlike the nSSR dataset, the Davey catchment (Figure 2 and Table 3). Above average Ar were observed in all but one population from these catchments. Four populations in the Gordon/ Denison catchment, especially Gordon River - 15 km upstream and Sir John Falls, were notable for their consistently high diversity in terms of Na , Ho , He and Ar (Figure 2, Table S6). Identical to the nSSR dataset, lowest Ar levels were observed at Yellow Creek and Stanley River (Pieman catchment) and the isolated Lake Judd in the Huon catchment. Tahune and Southwood were found to have above average Ar for population outside the core range catchments (Figure 2). Private alleles were found in 22 of the 33 populations from across all catchments with a maximum of 7 per population (Figure 3). The highest numbers of private alleles were observed in the Pieman and Huon/ Picton catchments with 18 and 14, respectively (Figure 3, Table S5). Populations with the most private alleles were mostly restricted to these catchments with 4 in Wilson River, Stanley River (both Pieman), Lake Judd (Huon/ Picton) and Erebus/ Jane River Confluence (Franklin), 5 at Harman River and Corrina (Pieman) and 7 at Newell Creek (King). Geographic structure was moderate with an average Fst of 0.143, GstH of 0.118 and Josts’ D of 0.0197. The same geographic based genetic clusters as identified in the nSSR dataset were recovered but with greater resolution in the internal nodes of the neighbour joining tree (Figure 4). The MIG-seq dataset-based NJ tree also found greater clustering of Gordon/ Denison and Huon/ Picton catchment populations and resolved the position of the low diversity Lake Judd population as closest to the Davey, Gordon/ Denison and Huon/ Picton catchment populations rather being most closely related to the Franklin cluster as resolved using nSSRs. A significant association between genetic differentiation of populations with geographic distance, stronger than the nSSR dataset, was also found in the MIG-seq dataset (r= 0.4547, p = 0.0001 Mantel test) (Figure S2). Similar to the nSSR dataset, there was a significant correlation between elevation (masl) and Ar (r= -0.491, P = 0.005) due to a decline in Ar with elevation (Figure 5). One-way ANOVA tests showed that differences in genetic diversity between catchments (Table S11) were statistically significant ( P < 0.05) (Table S12) for all genetic diversity indices except PAr and Fis . No other statistically significant differences were observed for the other groupings (i.e. disturbance history and stand type, Figure 6) except for Ne and Ho for comparisons between stand types. Population prioritization for conservation Similar patterns were observed for both nSSR and MIG-seq datasets for the Gordon/ Denison catchment populations and populations in the lower Franklin catchment (Figure 7) whereby removal of these population resulted in losses of overall within-population allelic diversity. On the other hand, results for the other catchments were mostly inconsistent between nSSR and MIG-seq datasets. All populations in the Pieman catchment were notable for high losses after removal of overall between-population allelic diversity in the MIG-seq dataset (Figure 7). Discussion Nuclear SSRs and MIG-seq datasets revealed concordant patterns but MIG-seq provided greater resolution of internal relationships between genetic clusters and more reliable estimates of population level genetic diversity. This is likely due to the increased number of loci (Sunde et al. , 2020), the potential for convergence of allele size in SSRs (homoplasy) (Coates et al. , 2009; Munoz et al. , 2017) and the bi-allelic nature of SNPs, whose genetic differentiation between populations is more impacted by genetic drift than multi-allelic microsatellites (Haasl and Payseur, 2011). Overall, despite sampling nearly all known primary stands of L. franklinii this study found no significant differences in genetic diversity between primary and disturbed stands. In addition, contrary to expectations that stands more accessible to past logging at lower elevations would show reduced genetic diversity, non-riverine stands showed no difference to riverine stands in terms of rarefied allelic richness and rarefied allelic richness decreased with increasing elevation of sampled stands. Rather than post-colonial human impact, this study of the range-wide genetic diversity of L. franklinii , indicate that factors pre-dating European colonization have had the most impact on the species’ genetic diversity. These factors include (1) the role of limited realised gene flow in shaping genetic patterns in the species as evidenced by the significant isolation by geographic distance across the species range; (2) a core-edge pattern of genetic diversity whereby populations in the core of the species range, particularly the lower Gordon and Franklin Rivers, had the greatest genetic diversity, contrasting with populations in catchments at the edge of the species range which mostly had lower diversity forming unique genetic clusters with high numbers of unique alleles; and (3) the presence of distinct genetic lineages restricted to catchments that were previously undetected using lower resolution isozymes (Shapcott, 1997) and Sanger sequence based chloroplast haplotypes (Clark and Carbone, 2008), that is most likely a legacy of long term geographic isolation probably since the Last Glacial. In comparison to other Tasmanian fire sensitive conifers with range-wide nuclear SSR based studies available ( Athrotaxis cupressoides , Diselma archeri and Pherosphaera hookeriana ; Worth et al. , 2017, 2021), L. franklinii has the lowest mean Ne and mean Ho suggesting that the species may have undergone greater past reductions in effective population size. However, more robust conclusions could be gained by comparing species level genetic diversity using a marker with a greater number of loci, such as MIG-seq. Maintenance of genetic diversity in disturbed stands Although we found no evidence for loss of genetic diversity due to post-colonisation human impacts, some losses are likely to have occurred that could not be detected in this study, given the high number of population specific alleles and the fact that some large stands have been completely lost during dam construction and inundation, including one of the most extensive areas of primary forest in the upper Gordon River (Kerr and McDermott, 1999). The maintenance of high genetic diversity in disturbed stands, including the highest diversity stands found in the lower Gordon and Franklin catchments, may be due to the nature of past logging of the species targeting intermediate size classes and restricted to areas within reach of the riverbank, so that most logged areas remained mostly intact (Farjon and Page, 1999). Misshapen trees were also left standing including some of the oldest individuals (Kerr and McDermott, 1999). The selective nature of past logging is well demonstrated by stumps at a site in northwest Tasmania (Sunday Creek) probably from the early 20 th century which demonstrates how trees of 600-900 mm basal diameter and particularly the >900 mm class were cut but many trees <600 mm were left standing (Millington et al. , 1979). Origin of the core-edge pattern of genetic diversity The high diversity of the core range is most likely associated with the formation of the species’ modern range. As evidenced by fossil pollen evidence, L. franklinii was more common during the Last Glacial than at present (Van De Geer et al. , 1994; De Deckker et al. , 2019) and may have had a wider ecological niche occurring not only in riverine sites but as a shrub in non-riverine sites into the subalpine zone (Macphail and Colhoun, 1985; Colhoun et al. , 1999; Colhoun and Shimeld, 2012). The species expanded to occupy its current distribution along lowland rivers in the mid to late Holocene as indicated by the rapid increase in its pollen during this time (Colhoun et al. , 1991; Van De Geer et al. , 1991; Colhoun et al. , 1993; Fletcher and Thomas, 2007a; Fletcher et al. , 2021) expanding into already established closed Nothofagus cunninghamii rainforests (Macphail, 1981). This expansion most likely occurred from scattered Last Glacial populations that occurred from upland areas, where the species occurred as a rare shrub in the subalpine zone, down to the now submerged coastline where, even below the Last Glacial depressed tree line, tall closed canopy rainforests probably did not occur (Colhoun, 1985). Given the widespread occurrence of the species prior to and during the Last Glacial (Colhoun and Goede, 1979; Colhoun, 1980; Colhoun and van de Geer, 1986; Jordan et al. , 1991; Colhoun et al. , 1999; Cooley et al. , 2024), most of the species current range was likely shaped by fragmentation during the Holocene. The mid-late Holocene expansion of open fire-dependent vegetation communities such dry forest and moorland (Jackson, 1999; Fletcher and Thomas, 2010) within the distribution of L. franklinii , likely occurred due to an increasingly variable and dry climate (Fletcher et al. , 2018) and an increase in fire frequency across the landscape via the arrival of Aboriginal people in the Last Glacial resulting in a new ignition source (Henríquez et al. , 2023). Despite Aboriginal people’s skillful application of fire use for promoting growth of fodder for marsupial herbivore game, burning of fire sensitive species such as L. franklinii may have occurred during extreme climatic events (Fletcher et al. , 2014) or when non-moorland vegetation was burnt for track construction (Colhoun, 1996). The increasingly dry and fire-prone landscape excluded L. franklinii from a large proportion of its potential climatic range (Read and Busby, 1990) almost confining the species to the fire protection afforded by waterbodies such as rivers and lakes. At this time, the Gordon and Franklin areas may have become the core of the species range due to a suitable climate and a lack of firing relative to other areas. Cryptic genetic divergence in the species core range The existence of a distinct genetic cluster apparent in two independently evolving nuclear markers in the Franklin River catchment was unexpected due to its geographical proximity to the Gordon/ Denison catchment. The origin of this cryptic divergence is uncertain but is most likely a result of postglacial migration from distinct refugial populations within these catchments to occupy its current range. Alternative explanations could involve historical logging or postglacial range contraction due to fire reducing genetic diversity leading to the differentiation of populations in these river systems. However, this is unlikely because these impacts were spread over the whole species’ range and the lower levels of diversity in the Huon/ Picton versus the Franklin catchment has not led to a unique genetic cluster in the former catchment. The high capacity for dispersal downstream by water due to the high flotation capacity of seed but extremely limited lateral seed dispersal by wind away from water (Shapcott, 1991) has probably prevented the two lineages from mixing. In addition, similar to the Tasmanian endemic conifer Pherosphaera hookeriana (Podocarpaceae) which has a similarly high level of genetic structuring across its narrow range (Worth et al . 2021), L. franklinii is under-represented in pollen rain (Fletcher and Thomas, 2007b) indicating potential for restricted pollen-mediated gene flow further limiting mixing of gene pools. Conservation implications Overall, this study has shown that the extensive and mostly unregulated selective logging across the range of L. franklinii from the early 1800s to late-1900s did not result in any detectable decline in genetic diversity. Therefore, in terms of genetic diversity alone both logged and primary forests have similar conservation value. In the present day, the most pressing threat to the species is the increase in frequency and severity of dry lightning induced wildfires over the last two decades within the distribution of L. franklinii (Styger et al. , 2018; Bowman et al. , 2022). Other palaeoendemic species have undergone significant losses including the destruction of approximately 1% of the range of the fire sensitive conifer Athrotaxis cupressoides in 2016 wildfires (Bowman et al. , 2021). Fires in February 2025 burnt the edges L. franklinii forests including the Harman River primary stand sampled in this study but did not penetrate far across the rainforest edge only scorching a few trees outside the main stand (Department of Natural Resources and Environment Tasmania, Tasmanian Government, 2025). Conserving the distinct genetic clusters identified in this study should be a priority for management that could involve physical protection during fire events and collection of seed for in situ / ex situ plantings. The Pieman catchment in particular is of special conservation significance consisting of mostly primary stands and forming a unique genetic cluster with consistently high level of unique alleles. Declarations Acknowledgements We would like to thank Jayne Balmer, Miguel De Salas, Alex McWhirter, Pierre Feutry, Terry Reid, Jarrah Vercoe, Mary Williams and Ray Worth for their assistance with field work , the Department of Primary Industries, Parks, Water and Environment, Tasmanian Government, for providing collection permits (TFL16005, TFL17332 and TFL20203) and Hiroko Kanahara, Chisako Furusawa and Hiroya Taguchi for assistance with laboratory work. Author contributions JRPW, JM and YS contributed to the study concept and design. Sample collection was performed by JRPW, JM and GJJ. Analyses were performed by JRPW with advice from JM, GJJ and YS. The first draft of the manuscript was written by JRPW with contributions from GJJ and JM. All authors read and approved the final manuscript. Funding This research was funded by JSPS Kakenhi grants 19H02980 and 23H00337. Data availability The nuclear SSR and MIG-seq SNP data files used for analyses during the current study are available as supplementary files. Competing interest The authors declare no competing interests. References Adeleye MA, Haberle SG, Bowman DM (2023). Long-term stability of temperate Australian wet forest-moorland mosaics despite recurrent fires associated with late Holocene climate change. Landscape Ecology 38 : 2675–2685. Anker SA, Colhoun EA, Barton CE, Peterson M, Barbetti M (2001). Holocene vegetation and paleoclimatic and paleomagnetic history from Lake Johnston, Tasmania. Quaternary Research 56 : 264–274. Biffin E, Conran JG, Lowe AJ (2011). Podocarp evolution: a molecular phylogenetic perspective. Smithsonian Contributions to Botany 95(95) Bolger AM, Lohse M, Usadel B (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 : 2114–2120. Bowman DM, Kolden CA, Williamson GJ (2022). Bushfires in Tasmania, Australia: an introduction. Fire 5 : 33. Bowman DM, Rodriguez-Cubillo D, Prior LD (2021). The 2016 Tasmanian wilderness fires: fire regime shifts and climate change in a Gondwanan biogeographic refugium. In: Ecosystem collapse and climate change , Springer, pp 133–153. Brown MJ, Brodribb TJ, Jordan GJ (2021). No‐analogue associations in the fossil record of southern conifers reveal conservatism in precipitation, but not temperature axes. Global Ecology and Biogeography 30 : 2455–2466. Buckley BM (1997). Climate variability in Tasmania based on dendroclimatic studies of Lagarostrobos Franklinii . (Doctoral dissertation, University of Tasmania). Buckley B, Cook E, Peterson M, Barbetti M (1997). A changing temperature response with elevation for Lagarostrobos franklinii in Tasmania, Australia. Climatic Change at High Elevation Sites : 245–266. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013). Stacks: an analysis tool set for population genomics. Molecular Ecology 22 : 3124–3140. Chapuis M-P, Estoup A (2007). Microsatellite null alleles and estimation of population differentiation. Molecular Biology and Evolution 24 : 621–631. Clark CM, Carbone I (2008). Chloroplast DNA phylogeography in long-lived Huon pine, a Tasmanian rain forest conifer. Canadian Journal of Forest Research 38 : 1576–1589. Coates BS, Sumerford DV, Miller NJ, Kim KS, Sappington TW, Siegfried BD, et al. (2009). Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. Journal of Heredity 100 : 556–564. Colhoun E (1980). Quaternary fluviatile deposits from the Pieman Dam site, western Tasmania. Proceedings of the Royal Society of London Series B Biological Sciences 207 : 355–384. Colhoun EA (1985). Pre‐last glaciation maximum vegetation history at Henty Bridge, western Tasmania. New Phytologist 100 : 681–690. Colhoun EA (1996). Application of Iversen’s glacial-interglacial cycle to interpretation of the late last glacial and Holocene vegetation history of western Tasmania. Quaternary Science Reviews 15 : 557–580. Colhoun EA, Benger SN, Fitzsimons SJ, Van De Geer G, Hill RS (1993). Quaternary organic deposit from Newton Creek valley, Western Tasmania. Australian Geographical Studies 31 : 26–38. Colhoun EA, van de Geer G (1986). Holocene to middle last glaciation vegetation history at Tullabardine Dam, western Australia. 229 : 177–207. Colhoun EA, van de Geer G, Fitzsimons SJ (1991). Late glacial and Holocene vegetation history at Governor Bog, King Valley, western Tasmania, Australia. Journal of Quaternary Science 6 : 55–66. Colhoun EA, Goede A (1979). The late Quaternary deposits of Blakes Opening and the middle Huon valley, Tasmania. Philosophical Transactions of the Royal Society of London B: Biological Sciences 286 : 371–395. Colhoun EA, Pola JS, Barton CE, Heijnis H (1999). Late Pleistocene vegetation and climate history of Lake Selina, western Tasmania. Quaternary International 57 : 5–23. Colhoun EA, Shimeld PW (2012). Late-Quaternary vegetation history of Tasmania from pollen records. In: Terra Australis , ANU E Press, Canberra, Australia, pp 297–328. Cooley S, Fletcher M-S, Lisé-Pronovost A, May J-H, Mariani M, Gadd P, et al. (2024). Rainforest response to glacial terminations before and after human arrival in Lutruwita (Tasmania). Quaternary Science Reviews 329 : 108572. Corander J, Waldmann P, Sillanpää MJ (2003). Bayesian analysis of genetic differentiation between populations. Genetics 163 : 367–374. De Deckker P, van der Kaars S, Macphail MK, Hope GS (2019). Land-sea correlations in the Australian region: 460 ka of changes recorded in a deep-sea core offshore Tasmania. Part 1: the pollen record. Australian Journal of Earth Sciences 66 : 1–15. Department of Natural Resources and Environment Tasmania, Tasmanian Government (2025). Ancient Huon pines confirmed intact following fire impact assessments. Eckenwalder JE (2009). Conifers of the world: the complete reference . Timber Press. El Mousadik A, Petit RJ (1996). High level of genetic differentiation for allelic richness among populations of the argan tree [ Argania spinosa (L.) Skeels] endemic to Morocco. Theoretical and Applied Genetics 92 : 832–839. Excoffier L, Hofer T, Foll M (2009). Detecting loci under selection in a hierarchically structured population. Heredity 103 : 285–298. Farjon A, Page C (Eds.) (1999). Conifers. Status Survey and Conservation Action Plan. IUCN, Gland, Switzerland and Cambridge, UK. Fletcher M-S, Bowman DM, Whitlock C, Mariani M, Hopf F, Benson A, et al. (2021). The influence of climatic change, fire and species invasion on a Tasmanian temperate rainforest system over the past 18,000 years. Quaternary Science Reviews 260 : 106824. Fletcher M-S, Bowman D, Whitlock C, Mariani M, Stahle L (2018). The changing role of fire in conifer-dominated temperate rainforest through the last 14,000 years. Quaternary Science Reviews 182 : 37–47. Fletcher M, Romano A, Lisé‐Pronovost A, Mariani M, Henriquez W, Gadd P, et al. (2024). Reconciling 22,000 years of landscape openness in a renowned wilderness. Geographical Research 62 , 503-525. Fletcher M, Thomas I (2007a). Holocene vegetation and climate change from near Lake Pedder, south‐west Tasmania, Australia. Journal of Biogeography 34 : 665–677. Fletcher MS, Thomas I (2007b). Modern pollen-vegetation relationships in western Tasmania, Australia. Review of Palaeobotany and Palynology 146 : 146–168. Fletcher MS, Thomas I (2010). The origin and temporal development of an ancient cultural landscape. Journal of Biogeography 37 : 2183–2196. Fletcher MS, Wolfe BB, Whitlock C, Pompeani DP, Heijnis H, Haberle SG, et al. (2014). The legacy of mid-Holocene fire on a Tasmanian montane landscape. Journal of Biogeography 41 : 476–488. Foll M, Gaggiotti O (2008). A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180 : 977–993. Gibson N (1988). A description of the Huon Pine ( Lagarostrobus franklinii (Hook. F.) C. J. Quinn) forest of the Prince of Wales and King Billy Ranges. Papers and proceedings of the royal society of Tasmania 122 : 127–133. Gibson N, Davies J, Brown M (1991). The ecology of Lagarostrobos franklinii (Hook. f.) Quinn (Podocarpaceae) in Tasmania. 1. Distribution, floristics and environmental correlates. Australian journal of ecology 16 : 215–222. Haasl RJ, Payseur BA (2011). Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites. Heredity 106 : 158–171. Hedrick PW (2005). A standardized genetic differentiation measure. Evolution 59 : 1633–1638. Henríquez WI, Fletcher M-S, Romano A (2023). Vegetation, fire and climate history in central-western Tasmania (41°S), Australia, over the last∼ 21,000 years. Quaternary Science Reviews 318 : 108315. Hill RS, MacPhail MK (1985). A Fossil Flora from rafted Plio-Pleistocene mudstones at Regatta Point, Tasmania. Australian Journal of Botany 33 : 497–517. Holbeck M (2022). Treasure in the trees: ancient stand of Huon pines ‘discovered’. Australian Geographic . Holm S (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics : 65–70. Holz A, Wood SW, Veblen TT, Bowman DMJS (2015). Effects of high‐severity fire drove the population collapse of the subalpine Tasmanian endemic conifer Athrotaxis cupressoides . Global change biology 21 : 445–458. Holz A, Wood SW, Ward C, Veblen TT, Bowman DMJS (2020). Population collapse and retreat to fire refugia of the Tasmanian endemic conifer Athrotaxis selaginoides following the transition from Aboriginal to European fire management. Global Change Biology 26 : 3108–3121. Huson DH, Bryant D (2006). Application of phylogenetic networks in evolutionary studies. Molecular Biology and Evolution 23 : 254–267. Jackson W (1999). The Tasmanian legacy of man and fire. Papers and proceedings of the Royal Society of Tasmania 133 : 1–14. Jeffreys H (1998). The Theory of Probability . OUP Oxford. Jombart T (2008). adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24 : 1403–1405. Jordan GJ, Carpenter RJ, Hill RS (1991). Late Pleistocene vegetation and climate near Melaleuca Inlet, south-western Tasmania. Australian Journal of Botany 39 : 315–333. Jost LOU (2008). GST and its relatives do not measure differentiation. Molecular Ecology 17 : 4015–4026. Kalinowski ST (2005). hp‐rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Molecular Ecology Notes 5 : 187–189. Kerr GJ, McDermott H (1999). The Huon Pine Story: The history of harvest and use of a unique timber . Mainsail Books. Khan R, Hill RS, Liu J, Biffin E (2023). Diversity, distribution, systematics and conservation status of Podocarpaceae. Plants 12 : 1171. Kirkpatrick JB, Styger JK, Marsden-Smedley JB (2018). Impact of changes in lightning fire incidence on the values of the Tasmanian wilderness world heritage area. In: Vol 152, pp 27–32. Langella O (2011). Populations 1.2. 31 CNRS UPR9034. Ledig FT (1992). Human impacts on genetic diversity in forest ecosystems. Oikos 63 : 87. López‐Cortegano E, Pérez‐Figueroa A, Caballero A (2019). metapop2: Re‐implementation of software for the analysis and management of subdivided populations using gene and allelic diversity. Molecular Ecology Resources 19 : 1095–1100. Lotterhos KE, Whitlock MC (2014). Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Molecular Ecology 23 : 2178–2192. Macphail MK (1981). A history of fire in the forest. Fire and Forest Management in Tasmania: Tasmanian Conservation Trust, Hobart 15 : 22. Macphail MK, Colhoun EA (1985). Late last glacial vegetation, climates and fire activity in southwest Tasmania. Search 16 : 43–45. Marthick JR, Larcombe MJ, Worth JRP (2020). Development of EST microsatellite markers for the Tasmanian palaeoendemic conifer Lagarostrobos franklinii ( Hook . f .) Quinn ( Podocarpaceae ). Silvae Genetica 69 : 1–5. Marthick JR, Phipps AJ, Jordan GJ, Takahashi D, Suyama Y, Worth JRP (submitted). Stand structure and genetic diversity in one of the world’s oldest conifer clones, Lagarostrobos franklinii at Mt Read, western Tasmania. Millington RJ, Jones R, Brown D, Vernon B (1979). Huon pine - endangered? Environmental Studies Occasional Paper . University of Tasmania, Hobart. Morales-Hidalgo D, Oswalt SN, Somanathan E (2015). Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. Forest Ecology and Management 352 : 68–77. Mosseler A, Major J, Rajora O (2003). Old-growth red spruce forests as reservoirs of genetic diversity and reproductive fitness. Theoretical and Applied Genetics 106 : 931–937. Munoz I, Henriques D, Jara L, Johnston JS, Chávez‐Galarza J, De La Rua P, et al. (2017). SNP s selected by information content outperform randomly selected microsatellite loci for delineating genetic identification and introgression in the endangered dark European honeybee ( Apis mellifera mellifera ). Molecular Ecology Resources 17 : 783–795. Nei M, Tajima F, Tateno Y (1983). Accuracy of estimated phylogenetic trees from molecular data. Journal of Molecular Evolution 19 : 153–170. Paradis E (2010). pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26 : 419–420. Parviainen J (2005). Virgin and natural forests in the temperate zone of Europe. Forest Snow and Landscape Research 79 : 9–18. Peakall ROD, Smouse PE (2006). GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Resources 6 : 288–295. Pelz K, Hayward G, Gray A, Berryman E, Woodall C, Nathanson A, et al. (2023). Quantifying old-growth forest of United States Forest Service public lands. Forest Ecology and Management 549 : 121437. Peterson MJ (1990). Distribution and conservation of Huon pine . Forestry Commission: Hobart, Tasmania. Petit RJ, Mousadik AE, Pons O (1998). Identifying populations for conservation on the basis of genetic markers. Conservation Biology 12 : 844–855. Potapov P, Yaroshenko A, Turubanova S, Dubinin M, Laestadius L, Thies C, et al. (2008). Mapping the world’s intact forest landscapes by remote sensing. Ecology and Society 13 . Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81 : 559–575. R Core Team (2022). R Core team 2021 R: A language and environment for statistical computing. R: A language and environment for statistical computing . Raymond M, Rousset F (1995). GENEPOP (version 1.2): population genetics software for Windows and Linux. Molecular Ecology Resources 8 : 103–106. Read J, Busby J (1990). Comparative responses to temperature of the major canopy species of Tasmanian cool temperate rain-forest and their ecological significance II. Net photosynthesis and climate analysis. Australian Journal of Botany 38 : 185–205. Rochette NC, Rivera-Colón AG, Catchen JM (2019). Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Molecular Ecology 28 : 4737–4754. Roque RH, Sebbenn AM, Boshier DH, Filho AF, Tambarussi EV (2023). Logging affects genetic diversity parameters in an Araucaria angustifolia population: an endangered species in southern Brazil. Forests 14 : 1046. Schaberg PG, DeHayes DH, Hawley GJ, Nijensohn SE (2008). Anthropogenic alterations of genetic diversity within tree populations: Implications for forest ecosystem resilience. Forest ecology and management 256 : 855–862. Shapcott A (1991). Dispersal and establishment of Huon pine ( Lagarostrobos franklinii ). Papers and Proceedings of the Royal Society of Tasmania 125 : 17–26. Shapcott A (1993). The population genetics of two temperate rainforest trees, Lagarostrobos franklinii (Hook f.) Quinn (Huon pine), and Atherosperma moschatum Labill. (Sassafras). Shapcott A (1997). Population genetics of the long-lived Huon pine Lagarostrobos franklinii : An endemic Tasmanian temperate rainforest tree. Biological Conservation 80 : 169–179. Styger J, Marsden-Smedley J, Kirkpatrick J (2018). Changes in lightning fire incidence in the Tasmanian Wilderness World Heritage Area, 1980–2016. Fire 1 : 38. Sunde J, Yıldırım Y, Tibblin P, Forsman A (2020). Comparing the performance of microsatellites and RADseq in population genetic studies: Analysis of data for pike ( Esox lucius ) and a synthesis of previous studies. Frontiers in Genetics 11 : 218. Suyama Y, Hirota SK, Matsuo A, Tsunamoto Y, Mitsuyuki C, Shimura A, et al. (2022). Complementary combination of multiplex high‐throughput DNA sequencing for molecular phylogeny . Wiley Online Library. Suyama Y, Matsuki Y (2015). MIG-seq: An effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. Scientific Reports 5 : 1–12. Tasmanian Planning Commission (2024). Tasmanian 2024 State of the Environment Report . The jamovi project (2025). jamovi (Version 2.6). Van De Geer G, Fitzsimons SJ, Colhoun EA (1991). Holocene vegetation history from King River railway bridge, western Tasmania. Papers and Proceedings - Royal Society of Tasmania 125 : 73–77. Van De Geer G, Heusser LE, Lynch‐Stieglitz J, Charles CD (1994). Paleoenvironments of Tasmania inferred from a 5–75 ka marine pollen record. Palynology 18 : 33–40. Widiyatno, Indrioko S, Na’iem M, Uchiyama K, Numata S, Ohtani M, et al. (2016). Effects of different silvicultural systems on the genetic diversity of Shorea parvifolia populations in the tropical rainforest of Southeast Asia. Tree Genetics & Genomes 12 : 1–12. Winter DJ (2012). MMOD: an R library for the calculation of population differentiation statistics. Molecular Ecology Resources 12 : 1158–1160. Worth JRP, Jordan GJ, Marthick JR, Sakaguchi S, Colhoun EA, Williamson GJ, et al. (2017). Fire is a major driver of patterns of genetic diversity in two co‐occurring Tasmanian palaeoendemic conifers. Journal of Biogeography 44 : 1254–1267. Worth JR, Marthick JR, Harrison PA, Sakaguchi S, Jordan GJ (2021). The palaeoendemic conifer Pherosphaera hookeriana (Podocarpaceae) exhibits high genetic diversity despite Quaternary range contraction and post glacial bottlenecking. Conservation Genetics 22 : 307–321. Tables Table 1 Details of the 33 populations of Lagarostrobos franklinii sampled including geographical location, stand type (either occurring along rivers/creeks, at the edge of lakes or non-riverine), the water catchment named after the largest river(s) (for the non-riverine stand this corresponded with catchment of the closest creeks), disturbance history (i.e. disturbed by fire/logging or primary) and the number of samples used for nuclear SSR and MIG-seq genotyping. No. Population Latitude Longitude Elevation (masl) Stand Type Catchment Disturbance history n for nSSR* n for MIG-seq 1 Yellow Ck. -41.589 145.357 524 River/ Creek Pieman primary 29 (1) 8 2 Harman Rv. -41.635 145.338 467 River/ Creek Pieman primary 29 (0) 8 3 Wilsons Rv. -41.647 145.382 203 River/ Creek Pieman primary 27 (0) 8 4 Stanley Rv. -41.704 145.291 243 River/ Creek Pieman disturbed 24 (0) 8 5 Pieman Rv. Corrina -41.654 145.091 14 River/ Creek Pieman disturbed 28 (1) 8 6 Newell Ck. -42.159 145.537 66 River/ Creek King disturbed 31 (2) 8 7 Teepookana Plateau -42.21 145.433 270 Non-riverine King disturbed 25 (0) 5 8 Bird Rv. -42.349 145.586 80 River/ Creek Bird disturbed 24 (0) 7 9 Loddon- Franklin conflu. -42.228 145.893 327 River/ Creek Franklin primary 19 (1) 7 10 Lake Vera -42.274 145.878 585 Lake Franklin primary 32 (1) 8 11 Lake Marilyn -42.284 145.875 710 Lake Franklin primary 31 (0) 8 12 Buckley's Chance -42.271 145.848 890 Non-riverine Franklin primary 28 (2) 8 13 Erebus- Jane conflu. -42.402 145.982 320 River/ Creek Franklin disturbed 29 (1) 7 14 Newlands Cascade -42.426 145.77 61 River/ Creek Franklin disturbed 20 (0) 8 15 Jane Rv. -42.435 145.805 87 River/ Creek Franklin disturbed 30 (0) 7 16 Kutikina Cave -42.527 145.768 40 River/ Creek Franklin disturbed 10 (0) 8 17 Gordon Rv. - 15km upstream -42.483 145.672 22 River/ Creek Gordon/ Denison disturbed 31 (0) 8 18 Sir John Falls -42.57 145.693 26 River/ Creek Gordon/ Denison disturbed 24 (3) 7 19 Denison- Gordon conflu. -42.717 145.829 44 River/ Creek Gordon/ Denison disturbed 18 (0) 8 20 Denison Rv. -42.611 145.992 147 River/ Creek Gordon/ Denison primary 29 (1) 6 21 Serpentine Gorge -42.761 145.969 256 River/ Creek Gordon/ Denison primary 29 (0) 8 22 Gilbert Leitch HPR -42.733 146.085 389 River/ Creek Gordon/ Denison primary 29 (0) 8 23 Davey Rv. -43.123 145.983 37 River/ Creek Davey disturbed 29 (0) 8 24 Badgers Ck. -43.1 146.034 311 River/ Creek Davey primary 13 (0) 8 25 Crossing Rv. -43.139 146.07 95 River/ Creek Davey disturbed 29 (1) 8 26 Condominium Ck. -42.954 146.369 383 River/ Creek Huon/ Picton primary 29 (1) 8 27 Lake Judd -42.971 146.43 609 Lake Huon/ Picton disturbed 28 (0) 8 28 Scotts Peak Dam -43.056 146.289 230 River/ Creek Huon/ Picton disturbed 29 (1) 8 29 Huon Rv. Gorge -43.104 146.503 157 River/ Creek Huon/ Picton disturbed 26 (1) 8 30 Farmhouse Ck. -43.232 146.668 174 River/ Creek Huon/ Picton disturbed 30 (0) 8 31 Riveaux Ck. -43.187 146.673 148 River/ Creek Huon/ Picton disturbed 19 (0) 8 32 Tahune -43.096 146.727 67 River/ Creek Huon/ Picton disturbed 33 (2) 8 33 Southwood -43.052 146.821 48 River/ Creek Huon/ Picton disturbed 30 (0) 8 * Denotes the number of clonal samples excluded from the nSSR analyses Table 2 Average genetic diversity statistics at the catchment level based on the nuclear SSR dataset. Catchment* Polymorphic loci (%) Ne Ho He uHe Fis Ar PAr Pieman (5) 100 2.084 0.489 0.473 0.482 -0.035 2.946 0.056 King (2) 100 2.209 0.503 0.507 0.517 0.019 3.610 0.080 Bird (1) 100 1.950 0.521 0.470 0.480 -0.098 2.940 0.050 Franklin (8) 100 2.274 0.515 0.509 0.521 -0.019 3.460 0.043 Gordon/Denison (6) 100 2.089 0.506 0.489 0.499 -0.029 3.565 0.055 Davey (3) 100 2.134 0.487 0.465 0.477 -0.049 3.100 0.060 Huon/Picton (8) 98.4 2.005 0.460 0.457 0.465 -0.005 2.983 0.029 Overall Average 99.8 2.106 0.497 0.481 0.492 -0.031 3.229 0.053 *The numbers in brackets indicate the number of populations sampled in each catchment Table 3 Average genetic diversity statistics at the catchment level based on the MIG-seq SNP dataset. Catchment Polymorphic loci (%) Ne Ho He uHe Fis Ar PAr Pieman (5) 39.57 1.205 0.126 0.125 0.133 -0.022 1.327 0.0042 King (2) 41.60 1.213 0.136 0.130 0.143 -0.053 1.359 0.0053 Bird (1) 45.33 1.235 0.150 0.142 0.154 -0.058 1.381 0.0024 Franklin (8) 47.32 1.238 0.147 0.145 0.155 -0.028 1.386 0.0011 Gordon/Denison (6) 50.10 1.242 0.155 0.150 0.161 -0.038 1.409 0.0012 Davey (3) 48.92 1.231 0.146 0.143 0.153 -0.022 1.389 0.0016 Huon/Picton (8) 44.62 1.220 0.138 0.135 0.145 -0.031 1.360 0.0021 Overall Average 45.35 1.227 0.143 0.139 0.149 -0.036 1.373 0.003 Additional Declarations There is no duality of interest Supplementary Files nuclearSSRgenepopfile.txt nuclear SSR genepop file SupplementaryMaterials.docx Supplementary Materials MIGseqgenepopfile.txt MIG-seq genepop file Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Heredity → Version 1 posted Editorial decision: revise 27 Jun, 2025 Review # 2 received at journal 26 Jun, 2025 Review # 1 received at journal 16 Jun, 2025 Reviewer # 2 agreed at journal 13 Jun, 2025 Reviewer # 1 agreed at journal 12 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 31 May, 2025 First submitted to journal 31 May, 2025 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. 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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-6792156","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469853996,"identity":"ffedb7e7-c4cd-46c7-bb0c-bcd2336798e7","order_by":0,"name":"James Worth","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3PMQrCMBSA4RcKdYl2fS56hUihOIheJaUQNx1cHBxSXINdFS8heIFCoS66O1YEZ0cFQasI4pJ2FMw/BBLy8RIAk+kHIzJfzpdOwwGSnd+HvJCQhRJuXVqteSnyzKJ24rP4QwquL3cpUmpxd5OQ6XgC3QjglGkfNhsIxLY98LYBCbcpBAsJfaYlinp7RunIi4ebQyghYDEILCTcRn8dHUlYnsQ281cYvEi3BKn1r6HiLu7zKTJFXp8W/KWlqol7u9wbTpRPkZNOz6kokWmJpF979KVFhU5AE74J9AAqqZaYTCbT3/UAM6RJ48gR9usAAAAASUVORK5CYII=","orcid":"","institution":"Forestry and Forest Products Research Institute","correspondingAuthor":true,"prefix":"","firstName":"James","middleName":"","lastName":"Worth","suffix":""},{"id":469853997,"identity":"1754101d-a701-4cdd-8547-de316e46346d","order_by":1,"name":"James Marthick","email":"","orcid":"","institution":"Royal Hobart Hospital","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Marthick","suffix":""},{"id":469853998,"identity":"dde2926a-96e9-441c-aec6-ce335c3de3f3","order_by":2,"name":"Yoshihisa Suyama","email":"","orcid":"https://orcid.org/0000-0002-3136-5489","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Yoshihisa","middleName":"","lastName":"Suyama","suffix":""},{"id":469853999,"identity":"c81b9032-3701-4dce-9420-ecea5e4a37dd","order_by":3,"name":"Gregory Jordan","email":"","orcid":"https://orcid.org/0000-0002-6033-2766","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Jordan","suffix":""}],"badges":[],"createdAt":"2025-05-31 16:50:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6792156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6792156/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41437-025-00798-2","type":"published","date":"2025-10-14T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84704264,"identity":"f281f3c7-1224-4039-b8a5-ac45864f685a","added_by":"auto","created_at":"2025-06-16 12:07:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":801141,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the 33 sampled \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e populations with the known distribution of the species indicated by small grey squares (sourced from Petersen 1990, the Natural Values Atlas of Tasmania and personal records). The populations are coloured according to the major river catchment they occur in. Populations are numbered according to Table 1 from north to south. Major rivers and waterbodies are shown along with blue shading indicating elevation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/c8175adbf00f9d872fb10cff.png"},{"id":84705311,"identity":"ea953c37-707f-46ae-8ef0-b58531afe485","added_by":"auto","created_at":"2025-06-16 12:15:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178678,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic diversity according to observed heterozygosity (\u003cem\u003eHo\u003c/em\u003e) (a) and rarefied allelic richness (\u003cem\u003eAr\u003c/em\u003e) (b) for all populations of \u003cem\u003eLagarostrobos franklinii\u003c/em\u003ebased on the eight nuclear SSR loci and 845 MIG-seq SNPs. The shaded areas delimit the seven catchments. Population numbers follow the order in Table 1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/f6c057bd46b2e25f639cd402.png"},{"id":84704265,"identity":"7849e45b-59c6-48db-bd40-fca7c50f05d5","added_by":"auto","created_at":"2025-06-16 12:07:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76551,"visible":true,"origin":"","legend":"\u003cp\u003eThe number of private alleles for all populations of \u003cem\u003eLagarostrobos franklinii\u003c/em\u003eidentified at the eight nuclear SSR loci and 845 MIG-seq SNP datasets. The shaded areas delimit the seven catchments. Population numbers follow the order in Table 1.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/168797b6f9d8680d1eaa8a96.png"},{"id":84704271,"identity":"f0f6a604-434f-4f11-92e8-344f72ea2b4e","added_by":"auto","created_at":"2025-06-16 12:07:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120742,"visible":true,"origin":"","legend":"\u003cp\u003eNeighbour joining tree depicting genetic relationships of the 33 sampled \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e populations based on Nei’s \u003cem\u003eDa\u003c/em\u003e distance for the nuclear SSR dataset and Nei’s distance for the MIG-seq dataset. Populations are coloured according to the catchment within which they occur with the high elevation non-riverine populations from Frenchmans Cap indicated.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/bdaf881a39629629ba4f888e.png"},{"id":84704272,"identity":"e7c42bee-40b4-414a-b060-90752ab3f7bf","added_by":"auto","created_at":"2025-06-16 12:07:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":79399,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs displaying correlation between population elevation (m asl) and \u003cem\u003eAr\u003c/em\u003e for the (a) nSSR and (b) MIG-seq datasets.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/e56827f06ad5039aa7e4a35d.png"},{"id":84705312,"identity":"0d78d65d-72cc-4413-a9a5-694922126dc8","added_by":"auto","created_at":"2025-06-16 12:15:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81135,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of genetic diversity means with 95% confidence intervals (blue bars) of observed heterozygosity (\u003cem\u003eHo\u003c/em\u003e), rarefied allelic richness (\u003cem\u003eAr\u003c/em\u003e) and rarefied private allelic richness (\u003cem\u003ePAr\u003c/em\u003e) between disturbed and primary stands based on nuclear SSR (a, b and c) and MIG-seq (d, e and f) markers. All differences were insignificant at the \u0026lt; \u003cem\u003eP\u003c/em\u003e = 0.05 level.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/0c7f2873fbae5fef2d76c696.png"},{"id":84704270,"identity":"9c804d24-9364-48d5-8303-937822f28579","added_by":"auto","created_at":"2025-06-16 12:07:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":136720,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of loss (+) or gain (-) of allelic diversity after removal of each population represented in terms of total allelic diversity (white diamond), average allelic diversity within populations (blue columns) and average allelic diversity between populations (white columns). The shaded areas delimit the seven catchments. Population numbers follow the order in Table 1.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/c415f3c115a57efe674e3646.png"},{"id":93559202,"identity":"ac4b0296-56bb-41be-bbe8-93f83515ed3f","added_by":"auto","created_at":"2025-10-15 07:17:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2509507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/cf637991-e429-49cf-9a1d-a6475b77680a.pdf"},{"id":84704263,"identity":"bbcb623c-644e-4677-9e4e-755bbccbb79d","added_by":"auto","created_at":"2025-06-16 12:07:47","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48235,"visible":true,"origin":"","legend":"nuclear SSR genepop file","description":"","filename":"nuclearSSRgenepopfile.txt","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/f0a807acc81eff624473071b.txt"},{"id":84704268,"identity":"782162f6-2903-4831-a587-c7ae1e5e5571","added_by":"auto","created_at":"2025-06-16 12:07:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":860024,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/93ae3d8497829065159002ae.docx"},{"id":84704267,"identity":"ea734969-628c-4849-af53-d713659234a9","added_by":"auto","created_at":"2025-06-16 12:07:47","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1083040,"visible":true,"origin":"","legend":"MIG-seq genepop file","description":"","filename":"MIGseqgenepopfile.txt","url":"https://assets-eu.researchsquare.com/files/rs-6792156/v1/975ba11a3de31007be219c80.txt"}],"financialInterests":"There is no duality of interest","formattedTitle":"\u003cp\u003eLast Glacial and Holocene dynamics override post-colonial disturbance in shaping genetic diversity of a heavily exploited palaeoendemic conifer, \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMost of the world\u0026rsquo;s forests have been impacted by agriculture, timber extraction and fire incursion, with only around 30% of the world\u0026rsquo;s forests considered to be primary and even lower percentages remaining in the northern hemisphere temperate zone (Parviainen, 2005; Pelz \u003cem\u003eet al.\u003c/em\u003e, 2023). Human disturbance has mostly had negative impacts on tree species genetic diversity (Ledig, 1992), with potential deleterious implications for these species\u0026rsquo; capacity to tolerate and/ or adapt to environmental change (Schaberg \u003cem\u003eet al.\u003c/em\u003e, 2008). In recognition of the need to protect forests and their intrinsic biodiversity and ecosystem services, the area of conserved forests has risen across the globe since the 1990s (Morales-Hidalgo \u003cem\u003eet al.\u003c/em\u003e, 2015). However, while direct disturbance by humans including timber extraction has largely ceased in these protected areas, the legacy of past human impacts on the amount and distribution of genetic diversity in forest tree species is poorly understood for most species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cool temperate island of Tasmania is the most forested state in Australia and retains 73.1% of its original native vegetation cover (Tasmanian Planning Commission, 2024). The western half of the island includes one of the few intact forest landscapes designated in southeastern Australia (Potapov \u003cem\u003eet al.\u003c/em\u003e, 2008), with extensive areas of intact native vegetation comprised of a mosaic of moorland, eucalyptus forests and cool temperate rainforests. However, during the early 19th to mid-20th centuries, cool temperate rainforests were selectively logged by post-colonial Europeans for the decay resistant, high-quality timber of Tasmanian endemic Podocarpaceae and Cupressaceae conifers. The extensive river systems of the region allowed access to conifer stands across their ranges and also a means of transporting logs downstream. The chief species targeted for logging was \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e (Hook. f.) Quinn (Podocarpaceae), or Huon pine, a slow growing, evergreen conifer distributed in western and southern Tasmania (Figure 1). It primarily occurs as a dominant tree of cool temperate rainforests along lowland rivers and the shores of sheltered sea inlets where it reaches heights of 10 \u0026ndash; 38 m (Eckenwalder, 2009) but also occurs at higher elevations near lakes and streams in the upper watershed of catchments. A few stands not associated with water bodies, including some near the subalpine zone (Gibson \u003cem\u003eet al.\u003c/em\u003e, 1991), are found in long unburnt areas (fire refugia) (Gibson, 1988). Of the estimated 10,500 ha of pre-colonial \u003cem\u003eL. franklinii\u003c/em\u003e forest, 15% has been lost to dam construction and fire (Farjon and Page, 1999) while only approximately 10% remains primary (Kerr and McDermott, 1999). Selective logging occurred across the species\u0026rsquo; range including in the core of its range, the rivers draining into Macquarie Harbour (Millington \u003cem\u003eet al.\u003c/em\u003e, 1979), and in other important occurrences along the Huon and Davey Rivers in the south and the Pieman catchment at the northern edge of its range. Logging of \u003cem\u003eL. franklinii\u003c/em\u003e declined as accessible trees were depleted, and concerns about the sustainability of the practice led to a ban in cutting green wood in the 1970s (Holbeck, 2022). Apart from the direct loss of trees, past logging may have resulted in an increase in burning of the landscape (Adeleye \u003cem\u003eet al.\u003c/em\u003e, 2023) due to the use of fire to open up the dense vegetation to ease land access to \u003cem\u003eL. franklinii\u003c/em\u003e stands. With no capacity to recover from fire \u003cem\u003eL. franklinii\u003c/em\u003e is likely to have been impacted with the complete loss of stands documented in some areas (Peterson, 1990; Gibson \u003cem\u003eet al.\u003c/em\u003e, 1991; Buckley \u003cem\u003eet al.\u003c/em\u003e, 1997) although its mostly riverine habitat has resulted in fewer losses than for non-riverine fire-sensitive Tasmanian conifers such as \u003cem\u003eAthrotaxis\u003c/em\u003e spp. (Holz \u003cem\u003eet al.\u003c/em\u003e, 2015, 2020). Most of the range of \u003cem\u003eL. franklinii\u003c/em\u003e is now protected in national parks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the species slow growth, fire sensitivity and high proportion of the species range that was selectively logged, impacts on genetic diversity and structure of \u003cem\u003eL. franklinii\u003c/em\u003e forests may have been severe. However, this species remains an important part of the rainforest canopy and whether a legacy of post-colonial human impacts is evident at the genetic level is unknown. In other forest trees, including conifers, genetic analyses have found that primary forests can harbour a greater diversity of rare alleles, which are crucial in determining species adaptive capacity (Petit \u003cem\u003eet al.\u003c/em\u003e, 1998), than logged forests (Mosseler \u003cem\u003eet al.\u003c/em\u003e, 2003; Widiyatno \u003cem\u003eet al.\u003c/em\u003e, 2016; Roque \u003cem\u003eet al.\u003c/em\u003e, 2023). This loss of rare alleles is due to fragmentation of stands and reduction in density of individuals reducing effective population size and, in the case of selective logging, the loss of older age classes. In Tasmania, range-wide genetic studies of fire-sensitive palaeoendemic conifers, \u003cem\u003eAthrotaxis cupressoides\u003c/em\u003e and \u003cem\u003eDiselma archeri\u003c/em\u003e, found significant reductions in stand-level genetic diversity with increasing past fire severity (Worth \u003cem\u003eet al.\u003c/em\u003e, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study investigates the genetic diversity, and its spatial distribution, within and between 33 stands of \u003cem\u003eL. franklinii\u003c/em\u003e from across the species\u0026rsquo; range. Twenty-one of the stands are recorded as having been selectively logged while 12 have remained untouched by past fires and selective logging based on Peterson (1990) and Kerr and McDermott (1999). Two genetic markers with contrasting mutational mechanisms were used: nuclear simple sequence repeats (SSRs) (Marthick \u003cem\u003eet al.\u003c/em\u003e, 2020) and genome wide single nucleotide polymorphisms (SNPs) obtained using multiplexed ISSR genotyping by sequencing (MIG-seq) (Suyama and Matsuki, 2015). Here we aim to examine the impact of post-colonial human disturbance on \u003cem\u003eL. franklinii\u003c/em\u003e including past logging on the species by comparing genetic diversity of: (1) primary stands untouched by past logging and fire versus non-primary stands; (2) stands at low elevation areas, i.e. more readily accessible to loggers, versus stands at less accessible higher elevations; and (3) more accessible riverine stands versus non-riverine ones. In addition, we assess whether past human disturbance may have differentially impacted the species genetic diversity across the major catchments containing the species. Lastly, we aim to identify populations whose loss would have the greatest impact on the species overall genetic diversity. Protecting remaining stands has become an increasing priority for \u003cem\u003eL. franklinii\u003c/em\u003e because the frequency and severity of lightning ignited fires (Kirkpatrick \u003cem\u003eet al.\u003c/em\u003e, 2018; Fletcher \u003cem\u003eet al.\u003c/em\u003e, 2024) have increased within the distribution of this highly fire-sensitive species.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eThe species\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLagarostrobos franklinii\u003c/em\u003e is a wind-pollinated, mostly dioecious conifer (Shapcott, 1993). Its seed are dispersed poorly by gravity, but may be dispersed over longer distances via water (Shapcott, 1997). The species is notable for being one of the longest lived trees in the world with individual stems reaching ages of more than 2500 years (Buckley, 1997) with clonal reproduction extending the lifespan of individual genets to potentially tens of millennia (Anker \u003cem\u003eet al.\u003c/em\u003e, 2001). \u003cem\u003eLagarostrobos\u0026nbsp;\u003c/em\u003eis an ancient Podocarpaceae genus (Biffin \u003cem\u003eet al.\u003c/em\u003e, 2011) that was widespread across the southern hemisphere in the Cretaceous and Palaeogene periods (Brown \u003cem\u003eet al.\u003c/em\u003e, 2021). The closest extant relative is the New Zealand endemic\u0026nbsp;\u003cem\u003eManoao colensoi\u003c/em\u003e (Hook. f.) Molloy, a monotypic genus erected from \u003cem\u003eLagarostrobos\u003c/em\u003e (Molloy 1995), with the two genera diverging 64 mya to 84 mya\u0026nbsp;(Biffin \u003cem\u003eet al.\u003c/em\u003e, 2011; Khan \u003cem\u003eet al.\u003c/em\u003e, 2023). The earliest definitive occurrence of fossils of the extant species is from the Early Pleistocene in western Tasmania\u0026nbsp;(Hill and MacPhail, 1985).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSampling\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 890 samples of \u003cem\u003eL. franklinii\u003c/em\u003e were collected from 33 populations (an average of 26.4 samples per population) spanning most of the species\u0026rsquo; range with the exception of the Spero/ Wanderer River area (Figure 1). The 33 populations represented the elevational range of the species, with the exception of the highest elevation stand at Mt Read which has been shown to consist of 4 clonal genotypes (Marthick \u003cem\u003eet al.\u003c/em\u003e, submitted) and is not considered in this study. Adult trees were collected at least 20 m apart to avoid sampling closely related individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLaboratory work and scoring\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDNA was extracted using the DNeasy 96 PlantKit (Qiagen) and eight dinucleotide repeat microsatellite loci used to genotype all 890 samples. These included six loci from (Marthick \u003cem\u003eet al.\u003c/em\u003e, 2020) (Huon_23318, Huon_50967, Huon_23863, Huon_18442, Huon_3945 and Huon_13112) and two developed for this study (Huon_7288: Forward primer 5` TTGCTTTGATTCTATTTGACGC\u0026ndash;3` and R primer 5` AAGGCTTTTCTGGAACACGA\u0026ndash;3` and Huon_50224: Forward primer 5` CATGTGCACCTAGCCAGAGA\u0026ndash;3` and R primer 5`CCTCCCTCCAACATAGCAGA\u0026ndash;3`). The PCR thermocycle followed Marthick \u003cem\u003eet al.\u003c/em\u003e (2020). The length of the amplified products was analysed using an ABI 3130xl Genetic Analyzer and fragments scored using the Microsatellite Plugin in Geneious 9.1.5 (https://www.geneious.com).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA subset of samples (254 samples from the 33 populations\u0026ndash; an average of 7.7 samples per population) representing the within-stand spatial extent of samples used for the nSSR analyses and excluding samples identified as clonal in the nSSR dataset were selected for genotyping using MIG-seq\u0026nbsp;(Suyama and Matsuki, 2015). The MIG-seq library was prepared using the protocol of Suyama \u003cem\u003eet al.\u003c/em\u003e, (2022) with a two-step PCR amplification process, after which all the amplicons larger than 250 bp were purified and sequenced on an Illumina MiSeq platform using a MiSeq Reagent Kit v3 (150 cycles, Illumina, San Diego, CA, USA). Trimmomatic 0.39 was used to eliminate low-quality and extremely short reads that contained adapter sequences (Bolger \u003cem\u003eet al.\u003c/em\u003e, 2014) after the first 17 bases of reads 1 and 2 (SSR primer regions and anchors) were skipped using \u0026quot;DarkCycle\u0026quot;. \u003cem\u003eDe novo\u003c/em\u003e assembly was performed from the 53,467 \u0026ndash; 304, 541 MIG-seq reads using Stacks 2.4 pipeline software (Catchen \u003cem\u003eet al.\u003c/em\u003e, 2013; Rochette \u003cem\u003eet al.\u003c/em\u003e, 2019) with default parameters. Next, loci filtering in the Stacks POPULATIONS program was undertaken as follows: the minimum percentage of shared loci to detect SNPs was set to 90%, the minimum number of minor alleles was set to 2, and the maximum allowable observed value of heterozygosity was set to 60%. The resulting 983 SNPs data set was then filtered to exclude loci with linkage disequilibrium coefficients below 0.6 in PLINK ver. 1.90 (Purcell \u003cem\u003eet al.\u003c/em\u003e, 2007), producing a final dataset consisting of 845 SNPs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData integrity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor all eight nuclear SSR loci, allelic correlations among loci (indicating linkage disequilibrium) were tested at the population level in Genepop v 4.2 (Raymond and Rousset, 1995) with sequential Bonferroni correction (p \u0026lt; 0.05) (Holm, 1979). In addition, FreeNA (Chapuis and Estoup, 2007) was used to test the potential for null alleles impacting estimates of genetic diversity and differentiation. This program estimates the null allele frequency at each locus and also calculates global \u003cem\u003eFst\u003c/em\u003e with and without null allele correction (Chapuis and Estoup, 2007). To detect if any nuclear SSR and MIG-seq SNP loci were potentially subject to selection the \u003cem\u003eFst\u003c/em\u003e outlier method, BayeScan v2.1 (Foll and Gaggiotti, 2008), was used with each run repeated three times. Existing genetic structuring can increase the number of false positives (Excoffier \u003cem\u003eet al.\u003c/em\u003e, 2009), therefore, the analyses were undertaken using genetic clusters detected in Bayesian Analysis of Population Structure (BAPS) version 6\u0026nbsp;(Corander \u003cem\u003eet al.\u003c/em\u003e, 2003)\u0026nbsp;implementing \u0026lsquo;clustering of groups of individuals\u0026rsquo;\u0026nbsp;with a maximum \u003cem\u003eK\u003c/em\u003e of 20. BAPS identified 13 clusters with probability of 0.79 for the nSSR dataset and 3 clusters for the MIG-seq SNP data set with a probability of 1 (Figure S3).\u0026nbsp;The false discovery rate of BayeScan can be impacted by demographic history (e.g. island model, isolation by distance or expansion from refugia), therefore, we tested\u0026nbsp;more realistic prior odds of 100 and 1,000 following (Lotterhos and Whitlock, 2014)\u0026nbsp;and assessed each locus under Jeffreys\u0026rsquo; scale of evidence (Jeffreys, 1998).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenetic diversity and structure analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the nuclear SSR dataset the extent of clonality was investigated in GenAlEx 6.5 (Peakall and Smouse, 2006) using the \u0026lsquo;find clones\u0026rsquo; function. If clonality was detected, subsequent analyses included only one individual from each clone. Genetic diversity indices (\u003cem\u003eNa\u003c/em\u003e, \u003cem\u003eNe\u003c/em\u003e, \u003cem\u003eHo\u003c/em\u003e, \u003cem\u003eHe\u003c/em\u003e, \u003cem\u003euHe\u003c/em\u003e and percent of polymorphic loci) were analysed in GenAlEx 6.5 at the species, population and locus level for both nuclear SSR and MIG-seq SNP datasets. Rarefied allelic richness (\u003cem\u003eAr\u003c/em\u003e), which is strongly influenced by the abundance of rare alleles (El Mousadik and Petit, 1996), and private allelic richness (\u003cem\u003ePAr\u003c/em\u003e) were calculated using a samples size of the smallest population minus one in HP Rare 1.1 (Kalinowski, 2005) for the nSSR dataset and Metapop2 v2.4.2 (L\u0026oacute;pez‐Cortegano \u003cem\u003eet al.\u003c/em\u003e, 2019) for the MIG-seq dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree measures of genetic differentiation, genetic differentiation among populations (\u003cem\u003eFst\u003c/em\u003e), Hedrick\u0026apos;s standardized (\u003cem\u003eG\u0026apos;stH\u003c/em\u003e)\u0026nbsp;(Hedrick, 2005)\u0026nbsp;and Jost\u0026rsquo;s D\u0026nbsp;(Jost, 2008)\u0026nbsp;were calculated in GenAlEx\u0026nbsp;6.5 and the mmod package\u0026nbsp;(Winter, 2012)\u0026nbsp;in R\u0026nbsp;(R Core Team, 2022). Neighbour joining trees were constructed in SplitsTree 4.14.4 (Huson and Bryant, 2006) using a matrix of Nei\u0026rsquo;s \u003cem\u003eD\u003csub\u003eA\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e(Nei \u003cem\u003eet al.\u003c/em\u003e, 1983)\u0026nbsp;calculated in Populations 1.2.32 (Langella, 2011)\u0026nbsp;for the nuclear SSR dataset and\u0026nbsp;a matrix of Nei\u0026rsquo;s distance calculated in the R program adegenet (Jombart, 2008) for the SNP dataset. For both datasets, isolation by distance (IBD) was tested for using the mantel.randtest command in adegenet package (Jombart, 2008) in R using 9999 permutations. For this purpose, decimal coordinates were converted to geodesic distances in pegas (Paradis, 2010) and a distance matrix calculated using Nei\u0026rsquo;s distance in adegenet. In addition, correlation between elevation and \u003cem\u003eAr\u003c/em\u003e were assessed in Jamovi 2.6.17 (The jamovi project, 2025) using the Pearson correlation coefficient for both datasets.\u003c/p\u003e\n\u003cp\u003eOne-way analysis of variance (ANOVA) undertaken in Jamovi 2.6.17 was used to determine if there were statistically significant differences between the means of genetic diversity indices (\u003cem\u003eNe\u003c/em\u003e, \u003cem\u003eNa\u003c/em\u003e, \u003cem\u003eHo\u003c/em\u003e, \u003cem\u003eHe\u003c/em\u003e, \u003cem\u003euHe\u003c/em\u003e, \u003cem\u003eAr\u003c/em\u003e, \u003cem\u003ePar\u003c/em\u003e and \u003cem\u003eFis\u003c/em\u003e) grouped according to catchment, stand type (riverine/creek, lake and not associated with water body) and human disturbance history (disturbed, primary). Here we treat the Franklin River and rivers/streams that feed it, a large tributary of the Gordon River, as a separate catchment because of the genetic distinctiveness of populations there (see Results).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePopulation prioritization for conservation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe contribution of each population to the overall species level allelic diversity, including its within- and between-population components, were calculated for both the nuclear microsatellite and MIG-seq datasets using Metapop2 v2.4.2 (L\u0026oacute;pez‐Cortegano \u003cem\u003eet al.\u003c/em\u003e, 2019). This program assesses the contribution of each population to overall species-level allelic diversity by assessing the impact of the removal of a given population on the global allelic diversity of the remaining populations (as a percentage of change in diversity).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eData integrity of nuclear SSR loci\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNineteen individuals were excluded to avoid analysing ramets of the same clone as detected by GenAlEx. Clonal individuals were found in 14 populations with between 1-3 individuals removed from those populations (Table 1). In total, 3 of the 924\u0026nbsp;population by locus-pairs were found to have significant allelic associations (results not shown) indicating there were no consistent linkage disequilibrium between loci across populations.\u0026nbsp;The estimates of null allele frequency were small, averaging 0.016 across all loci (between 0.007 to 0.028) (Table S1). Null alleles had negligible effect on overall genetic differentiation with global \u003cem\u003eFst\u003c/em\u003e including null alleles 0.100 compared to 0.099 when excluding null alleles (Table S1). Posterior probabilities were low for all nuclear SSR loci with an average across the three runs of 0.014 under prior odds of 100 and 0.00072 under prior odds of 1000\u0026nbsp;(Table S2) suggesting no evidence for either balancing or diversifying selection. Similarly, for MIG-seq loci posterior probabilities were low for all loci with an average of 0.0098 across the three runs under prior odds of 100 and 0.00099 under prior odds of 1000 (Table S3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNuclear SSR genetic diversity and structure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the nuclear SSR dataset, a total of 80 alleles were observed with the number of alleles per locus ranging from 4 to 18 (Table S4). The amount of missing data was 0.13%. The mean number of different alleles (\u003cem\u003eNa\u003c/em\u003e) and the number of effective alleles averaged over all loci (\u003cem\u003eNe\u003c/em\u003e) was 4.08 and 2.12, respectively (Table S4). Observed heterozygosity (\u003cem\u003eHo\u003c/em\u003e) and expected heterozygosity (\u003cem\u003eHe\u003c/em\u003e) across loci ranged between 0.38-0.62 (average = 0.49) and 0.36-0.61 (average = 0.48), respectively (Table S4). The inbreeding coefficient (\u003cem\u003eFis\u003c/em\u003e) values for each locus were all negative, varying between -0.05 to -0.01, except for\u0026nbsp;Huon_7288 (0.01).\u0026nbsp;\u003cem\u003eFst\u003c/em\u003e, \u003cem\u003eG\u0026apos;stH\u003c/em\u003e and Jost\u0026rsquo;s D values across loci ranged between 0.08-0.14, 0.11-0.29 and 0.06-0.21, respectively (Table S4).\u003c/p\u003e\n\u003cp\u003eGenetic diversity was distributed unevenly across the species range with highest \u003cem\u003eHo\u003c/em\u003e, \u003cem\u003eHe\u003c/em\u003e and \u003cem\u003eAr\u003c/em\u003e in the King, Bird, Franklin and Gordon/ Denison catchments (Figure 2 and Table 2). All but two populations with \u003cem\u003eAr\u003c/em\u003e above the overall population-level average were from these catchments, with highest \u003cem\u003eAr\u003c/em\u003e on the Franklin River at Kutikina Cave, Denison River and Jane River (Figure 2, Table S5). Lowest \u003cem\u003eAr\u003c/em\u003e values were observed in the northern and southern extremities of the range in the Pieman and Huon catchments including Yellow Creek, Stanley River and Lake Judd. However, some populations in these catchments had notably high genetic diversity including the Harman River (highest \u003cem\u003eHo\u003c/em\u003e) and above average \u003cem\u003eAr\u003c/em\u003e for Corrina and Southwood (Figure 2, Table S5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrivate alleles were found in 12 populations across all catchments with a maximum of 2 per population. Eight populations had one private allele while 2 were observed at Corrina, Newell Creek, Lake Marilyn and Denison River (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGeographic structure was moderate with an average \u003cem\u003eFst\u003c/em\u003e of 0.113, \u003cem\u003eGstH\u003c/em\u003e of 0.190 and Jost\u0026rsquo;s D of 0.105. Generally, geographically close populations were more closely related especially in the isolated Pieman and Davey catchments and the Franklin and King/ Bird catchments which formed distinct clusters in the neighbour joining (NJ) tree (Figure 4). On the other hand, populations from the Huon catchment were not all grouped together in the NJ tree with some closer to populations in the Gordon/ Denison catchment in the core of the species range. Genetic differentiation of populations was significantly associated with geographic distance (\u003cem\u003er\u003c/em\u003e= 0.2156, \u003cem\u003ep\u003c/em\u003e = 0.0186 - Mantel test, Figure S1). A significant correlation between elevation (m asl) and \u003cem\u003eAr\u003c/em\u003e was evident (\u003cem\u003er\u003c/em\u003e= -0.391, \u003cem\u003eP\u003c/em\u003e = 0.024) due to a decline in \u003cem\u003eAr\u003c/em\u003e with elevation (Figure 5).\u003c/p\u003e\n\u003cp\u003eApart from \u003cem\u003eNa\u003c/em\u003e, \u003cem\u003eAr\u003c/em\u003e, \u003cem\u003ePAr\u003c/em\u003e and \u003cem\u003eFis\u003c/em\u003e, all genetic diversity indices were higher on average for disturbed stands compared to primary stands (Table S7) but no differences were statistically significant (Figure 6 and Table S8). Similarly, for stand type no differences were statistically significant (Table S9-S10). In contrast, statistically significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were observed between catchment for mean \u003cem\u003eAr\u0026nbsp;\u003c/em\u003eand \u003cem\u003eHe\u003c/em\u003e (Table S11-S12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMIG-seq genetic diversity and structure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe overall amount of missing data was 4.03%. The genetic diversity at MIG-seq SNP loci was similar to the nSSR dataset with both \u003cem\u003eAr\u003c/em\u003e and \u003cem\u003eHo\u003c/em\u003e population-level values significantly correlated using the Pearson correlation coefficient (for \u003cem\u003eAr\u003c/em\u003e \u003cem\u003er\u003c/em\u003e = 0.686 and \u003cem\u003eP\u003c/em\u003e = 0.00001 and for \u003cem\u003eHo r\u003c/em\u003e = 0.374 and \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.0316). Highest \u003cem\u003eHo\u003c/em\u003e, \u003cem\u003eHe\u003c/em\u003e and \u003cem\u003eAr\u003c/em\u003e were observed in the Bird, Franklin, Gordon/ Denison catchments and, unlike the nSSR dataset, the Davey catchment (Figure 2 and Table 3). Above average \u003cem\u003eAr\u003c/em\u003e were observed in all but one population from these catchments. Four populations in the Gordon/ Denison catchment, especially Gordon River - 15 km upstream and Sir John Falls, were notable for their consistently high diversity in terms of \u003cem\u003eNa\u003c/em\u003e, \u003cem\u003eHo\u003c/em\u003e, \u003cem\u003eHe\u003c/em\u003e and \u003cem\u003eAr\u003c/em\u003e (Figure 2, Table S6). Identical to the nSSR dataset, lowest \u003cem\u003eAr\u003c/em\u003e levels were observed at Yellow Creek and Stanley River (Pieman catchment) and the isolated Lake Judd in the Huon catchment. Tahune and Southwood were found to have above average \u003cem\u003eAr\u003c/em\u003e for population outside the core range catchments (Figure 2).\u003c/p\u003e\n\u003cp\u003ePrivate alleles were found in 22 of the 33 populations from across all catchments with a maximum of 7 per population (Figure 3). The highest numbers of private alleles were observed in the Pieman and Huon/ Picton catchments with 18 and 14, respectively (Figure 3, Table S5). Populations with the most private alleles were mostly restricted to these catchments with 4 in Wilson River, Stanley River (both Pieman), Lake Judd (Huon/ Picton) and Erebus/ Jane River Confluence (Franklin), 5 at Harman River and Corrina (Pieman) and 7 at Newell Creek (King).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGeographic structure was moderate with an average \u003cem\u003eFst\u003c/em\u003e of 0.143, \u003cem\u003eGstH\u003c/em\u003e of 0.118 and Josts\u0026rsquo; D of 0.0197. The same geographic based genetic clusters as identified in the nSSR dataset were recovered but with greater resolution in the internal nodes of the neighbour joining tree (Figure 4). The MIG-seq dataset-based NJ tree also found greater clustering of Gordon/ Denison and Huon/ Picton catchment populations and resolved the position of the low diversity Lake Judd population as closest to the Davey, Gordon/ Denison and Huon/ Picton catchment populations rather being most closely related to the Franklin cluster as resolved using nSSRs. A significant association between genetic differentiation of populations with geographic distance, stronger than the nSSR dataset, was also found in the MIG-seq dataset (r= 0.4547, p = 0.0001 Mantel test) (Figure S2). Similar to the nSSR dataset, there was a significant correlation between elevation (masl) and \u003cem\u003eAr\u003c/em\u003e (r= -0.491, \u003cem\u003eP\u003c/em\u003e = 0.005) due to a decline in \u003cem\u003eAr\u003c/em\u003e with elevation (Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne-way ANOVA tests showed that differences in genetic diversity between catchments (Table S11) were statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table S12) for all genetic diversity indices except \u003cem\u003ePAr\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFis\u003c/em\u003e. No other statistically significant differences were observed for the other groupings (i.e. disturbance history and stand type, Figure 6) except for \u003cem\u003eNe\u003c/em\u003e and \u003cem\u003eHo\u003c/em\u003e for comparisons between stand types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePopulation prioritization for conservation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSimilar patterns were observed for both nSSR and MIG-seq datasets for the Gordon/ Denison catchment populations and populations in the lower Franklin catchment (Figure 7) whereby removal of these population resulted in losses of overall within-population allelic diversity. On the other hand, results for the other catchments were mostly inconsistent between nSSR and MIG-seq datasets. All populations in the Pieman catchment were notable for high losses after removal of overall between-population allelic diversity in the MIG-seq dataset (Figure 7).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNuclear SSRs and MIG-seq datasets revealed concordant patterns but MIG-seq provided greater resolution of internal relationships between genetic clusters and more reliable estimates of population level genetic diversity. This is likely due to the increased number of loci (Sunde \u003cem\u003eet al.\u003c/em\u003e, 2020), the potential for convergence of allele size in SSRs (homoplasy) (Coates \u003cem\u003eet al.\u003c/em\u003e, 2009; Munoz \u003cem\u003eet al.\u003c/em\u003e, 2017) and the bi-allelic nature of SNPs, whose genetic differentiation between populations is more impacted by genetic drift than multi-allelic microsatellites (Haasl and Payseur, 2011). Overall, despite sampling nearly all known primary stands of \u003cem\u003eL. franklinii\u003c/em\u003e this study found no significant differences in genetic diversity between primary and disturbed stands. In addition, contrary to expectations that stands more accessible to past logging at lower elevations would show reduced genetic diversity, non-riverine stands showed no difference to riverine stands in terms of rarefied allelic richness and rarefied allelic richness decreased with increasing elevation of sampled stands.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRather than post-colonial human impact, this study of the range-wide genetic diversity of \u003cem\u003eL. franklinii\u003c/em\u003e, indicate that factors pre-dating European colonization have had the most impact on the species\u0026rsquo; genetic diversity. These factors include (1) the role of limited realised gene flow in shaping genetic patterns in the species as evidenced by the significant isolation by geographic distance across the species range; (2) a core-edge pattern of genetic diversity whereby populations in the core of the species range, particularly the lower Gordon and Franklin Rivers, had the greatest genetic diversity, contrasting with populations in catchments at the edge of the species range which mostly had lower diversity forming unique genetic clusters with high numbers of unique alleles; and (3) the presence of distinct genetic lineages restricted to catchments that were previously undetected using lower resolution isozymes (Shapcott, 1997) and Sanger sequence based chloroplast haplotypes (Clark and Carbone, 2008), that is most likely a legacy of long term geographic isolation probably since the Last Glacial. In comparison to other Tasmanian fire sensitive conifers with range-wide nuclear SSR based studies available (\u003cem\u003eAthrotaxis cupressoides\u003c/em\u003e, \u003cem\u003eDiselma archeri\u003c/em\u003e and \u003cem\u003ePherosphaera hookeriana\u003c/em\u003e; Worth \u003cem\u003eet al.\u003c/em\u003e, 2017, 2021), \u003cem\u003eL. franklinii\u003c/em\u003e has the lowest mean \u003cem\u003eNe\u003c/em\u003e and mean \u003cem\u003eHo\u003c/em\u003e suggesting that the species may have undergone greater past reductions in effective population size. However, more robust conclusions could be gained by comparing species level genetic diversity using a marker with a greater number of loci, such as MIG-seq.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMaintenance of genetic diversity in disturbed stands\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough we found no evidence for loss of genetic diversity due to post-colonisation human impacts, some losses are likely to have occurred that could not be detected in this study, given the high number of population specific alleles and the fact that some large stands have been completely lost during dam construction and inundation, including one of the most extensive areas of primary forest in the upper Gordon River (Kerr and McDermott, 1999). The maintenance of high genetic diversity in disturbed stands, including the highest diversity stands found in the lower Gordon and Franklin catchments, may be due to the nature of past logging of the species targeting intermediate size classes and restricted to areas within reach of the riverbank, so that most logged areas remained mostly intact (Farjon and Page, 1999). Misshapen trees were also left standing including some of the oldest individuals (Kerr and McDermott, 1999). The selective nature of past logging is well demonstrated by stumps at a site in northwest Tasmania (Sunday Creek) probably from the early 20\u003csup\u003eth\u003c/sup\u003e century which demonstrates how trees of 600-900 mm basal diameter and particularly the \u0026gt;900 mm class were cut but many trees \u0026lt;600 mm were left standing (Millington \u003cem\u003eet al.\u003c/em\u003e, 1979).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOrigin of the core-edge pattern of genetic diversity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe high diversity of the core range is most likely associated with the formation of the species\u0026rsquo; modern range. As evidenced by fossil pollen evidence, \u003cem\u003eL. franklinii\u003c/em\u003e was more common during the Last Glacial than at present (Van De Geer \u003cem\u003eet al.\u003c/em\u003e, 1994; De Deckker \u003cem\u003eet al.\u003c/em\u003e, 2019) and may have had a wider ecological niche occurring not only in riverine sites but as a shrub in non-riverine sites into the subalpine zone (Macphail and Colhoun, 1985; Colhoun \u003cem\u003eet al.\u003c/em\u003e, 1999; Colhoun and Shimeld, 2012). The species expanded to occupy its current distribution along lowland rivers in the mid to late Holocene as indicated by the rapid increase in its pollen during this time (Colhoun \u003cem\u003eet al.\u003c/em\u003e, 1991; Van De Geer \u003cem\u003eet al.\u003c/em\u003e, 1991; Colhoun \u003cem\u003eet al.\u003c/em\u003e, 1993; Fletcher and Thomas, 2007a; Fletcher \u003cem\u003eet al.\u003c/em\u003e, 2021) expanding into already established closed \u003cem\u003eNothofagus cunninghamii\u003c/em\u003e rainforests (Macphail, 1981). This expansion most likely occurred from scattered Last Glacial populations that occurred from upland areas, where the species occurred as a rare shrub in the subalpine zone, down to the now submerged coastline where, even below the Last Glacial depressed tree line, tall closed canopy rainforests probably did not occur (Colhoun, 1985).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the widespread occurrence of the species prior to and during the Last Glacial (Colhoun and Goede, 1979; Colhoun, 1980; Colhoun and van de Geer, 1986; Jordan \u003cem\u003eet al.\u003c/em\u003e, 1991; Colhoun \u003cem\u003eet al.\u003c/em\u003e, 1999; Cooley \u003cem\u003eet al.\u003c/em\u003e, 2024), most of the species current range was likely shaped by fragmentation during the Holocene. The mid-late Holocene expansion of open fire-dependent vegetation communities such dry forest and moorland (Jackson, 1999; Fletcher and Thomas, 2010) within the distribution of \u003cem\u003eL. franklinii\u003c/em\u003e, likely occurred due to an increasingly variable and dry climate (Fletcher \u003cem\u003eet al.\u003c/em\u003e, 2018) and an increase in fire frequency across the landscape via the arrival of Aboriginal people in the Last Glacial resulting in a new ignition source (Henr\u0026iacute;quez \u003cem\u003eet al.\u003c/em\u003e, 2023). Despite Aboriginal people\u0026rsquo;s skillful application of fire use for promoting growth of fodder for marsupial herbivore game, burning of fire sensitive species such as \u003cem\u003eL. franklinii\u003c/em\u003e may have occurred during extreme climatic events (Fletcher \u003cem\u003eet al.\u003c/em\u003e, 2014) or when non-moorland vegetation was burnt for track construction (Colhoun, 1996). The increasingly dry and fire-prone landscape excluded \u003cem\u003eL. franklinii\u003c/em\u003e from a large proportion of its potential climatic range (Read and Busby, 1990) almost confining the species to the fire protection afforded by waterbodies such as rivers and lakes. At this time, the Gordon and Franklin areas may have become the core of the species range due to a suitable climate and a lack of firing relative to other areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCryptic genetic divergence in the species core range\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe existence of a distinct genetic cluster apparent in two independently evolving nuclear markers in the Franklin River catchment was unexpected due to its geographical proximity to the Gordon/ Denison catchment. The origin of this cryptic divergence is uncertain but is most likely a result of postglacial migration from distinct refugial populations within these catchments to occupy its current range. Alternative explanations could involve historical logging or postglacial range contraction due to fire reducing genetic diversity leading to the differentiation of populations in these river systems. However, this is unlikely because these impacts were spread over the whole species\u0026rsquo; range and the lower levels of diversity in the Huon/ Picton versus the Franklin catchment has not led to a unique genetic cluster in the former catchment. The high capacity for dispersal downstream by water due to the high flotation capacity of seed but extremely limited lateral seed dispersal by wind away from water (Shapcott, 1991) has probably prevented the two lineages from mixing. In addition, similar to the Tasmanian endemic conifer \u003cem\u003ePherosphaera hookeriana\u003c/em\u003e (Podocarpaceae) which has a similarly high level of genetic structuring across its narrow range (Worth \u003cem\u003eet al\u003c/em\u003e. 2021), \u003cem\u003eL. franklinii\u003c/em\u003e is under-represented in pollen rain (Fletcher and Thomas, 2007b) indicating potential for restricted pollen-mediated gene flow further limiting mixing of gene pools.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConservation implications\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOverall, this study has shown that the extensive and mostly unregulated selective logging across the range of \u003cem\u003eL. franklinii\u003c/em\u003e from the early 1800s to late-1900s did not result in any detectable decline in genetic diversity. Therefore, in terms of genetic diversity alone both logged and primary forests have similar conservation value. In the present day, the most pressing threat to the species is the increase in frequency and severity of dry lightning induced wildfires over the last two decades within the distribution of \u003cem\u003eL. franklinii\u0026nbsp;\u003c/em\u003e(Styger \u003cem\u003eet al.\u003c/em\u003e, 2018; Bowman \u003cem\u003eet al.\u003c/em\u003e, 2022). Other palaeoendemic species have undergone significant losses including the destruction of approximately 1% of the range of the fire sensitive conifer \u003cem\u003eAthrotaxis cupressoides\u003c/em\u003e in 2016 wildfires (Bowman \u003cem\u003eet al.\u003c/em\u003e, 2021). Fires in February 2025 burnt the edges \u003cem\u003eL. franklinii\u003c/em\u003e forests including the Harman River primary stand sampled in this study but did not penetrate far across the rainforest edge only scorching a few trees outside the main stand (Department of Natural Resources and Environment Tasmania, Tasmanian Government, 2025). Conserving the distinct genetic clusters identified in this study should be a priority for management that could involve physical protection during fire events and collection of seed for \u003cem\u003ein situ\u003c/em\u003e/ \u003cem\u003eex situ\u003c/em\u003e plantings. The Pieman catchment in particular is of special conservation significance consisting of mostly primary stands and forming a unique genetic cluster with consistently high level of unique alleles.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Jayne Balmer, Miguel De Salas, Alex McWhirter, Pierre Feutry, Terry Reid, Jarrah Vercoe, Mary Williams and Ray Worth for their assistance with field work\u003cem\u003e,\u0026nbsp;\u003c/em\u003ethe Department of Primary Industries, Parks, Water and Environment, Tasmanian Government, for providing collection permits (TFL16005, TFL17332 and TFL20203) and Hiroko Kanahara, Chisako Furusawa and Hiroya Taguchi for assistance with laboratory work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJRPW, JM and YS contributed to the study concept and design. Sample collection was performed by JRPW, JM and GJJ. Analyses were performed by JRPW with advice from JM, GJJ and YS. The first draft of the manuscript was written by JRPW with contributions from GJJ and JM. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by JSPS Kakenhi grants 19H02980 and 23H00337.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nuclear SSR and MIG-seq SNP data files used for analyses during the current study are available as supplementary files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdeleye MA, Haberle SG, Bowman DM (2023). Long-term stability of temperate Australian wet forest-moorland mosaics despite recurrent fires associated with late Holocene climate change. \u003cem\u003eLandscape Ecology\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e: 2675\u0026ndash;2685.\u003c/li\u003e\n \u003cli\u003eAnker SA, Colhoun EA, Barton CE, Peterson M, Barbetti M (2001). Holocene vegetation and paleoclimatic and paleomagnetic history from Lake Johnston, Tasmania. \u003cem\u003eQuaternary Research\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e: 264\u0026ndash;274.\u003c/li\u003e\n \u003cli\u003eBiffin E, Conran JG, Lowe AJ (2011). Podocarp evolution: a molecular phylogenetic perspective. \u003cem\u003eSmithsonian Contributions to Botany\u003c/em\u003e 95(95)\u003c/li\u003e\n \u003cli\u003eBolger AM, Lohse M, Usadel B (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e: 2114\u0026ndash;2120.\u003c/li\u003e\n \u003cli\u003eBowman DM, Kolden CA, Williamson GJ (2022). Bushfires in Tasmania, Australia: an introduction. \u003cem\u003eFire\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e: 33.\u003c/li\u003e\n \u003cli\u003eBowman DM, Rodriguez-Cubillo D, Prior LD (2021). The 2016 Tasmanian wilderness fires: fire regime shifts and climate change in a Gondwanan biogeographic refugium. In: \u003cem\u003eEcosystem collapse and climate change\u003c/em\u003e, Springer, pp 133\u0026ndash;153.\u003c/li\u003e\n \u003cli\u003eBrown MJ, Brodribb TJ, Jordan GJ (2021). No‐analogue associations in the fossil record of southern conifers reveal conservatism in precipitation, but not temperature axes. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e: 2455\u0026ndash;2466.\u003c/li\u003e\n \u003cli\u003eBuckley BM (1997). Climate variability in Tasmania based on dendroclimatic studies of \u003cem\u003eLagarostrobos Franklinii\u003c/em\u003e. (Doctoral dissertation, University of Tasmania).\u003c/li\u003e\n \u003cli\u003eBuckley B, Cook E, Peterson M, Barbetti M (1997). A changing temperature response with elevation for \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e in Tasmania, Australia. \u003cem\u003eClimatic Change at High Elevation Sites\u003c/em\u003e: 245\u0026ndash;266.\u003c/li\u003e\n \u003cli\u003eCatchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013). Stacks: an analysis tool set for population genomics. \u003cem\u003eMolecular Ecology\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e: 3124\u0026ndash;3140.\u003c/li\u003e\n \u003cli\u003eChapuis M-P, Estoup A (2007). Microsatellite null alleles and estimation of population differentiation. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e: 621\u0026ndash;631.\u003c/li\u003e\n \u003cli\u003eClark CM, Carbone I (2008). Chloroplast DNA phylogeography in long-lived Huon pine, a Tasmanian rain forest conifer. \u003cem\u003eCanadian Journal of Forest Research\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e: 1576\u0026ndash;1589.\u003c/li\u003e\n \u003cli\u003eCoates BS, Sumerford DV, Miller NJ, Kim KS, Sappington TW, Siegfried BD, \u003cem\u003eet al.\u003c/em\u003e (2009). Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. \u003cem\u003eJournal of Heredity\u003c/em\u003e \u003cstrong\u003e100\u003c/strong\u003e: 556\u0026ndash;564.\u003c/li\u003e\n \u003cli\u003eColhoun E (1980). Quaternary fluviatile deposits from the Pieman Dam site, western Tasmania. \u003cem\u003eProceedings of the Royal Society of London Series B Biological Sciences\u003c/em\u003e \u003cstrong\u003e207\u003c/strong\u003e: 355\u0026ndash;384.\u003c/li\u003e\n \u003cli\u003eColhoun EA (1985). Pre‐last glaciation maximum vegetation history at Henty Bridge, western Tasmania. \u003cem\u003eNew Phytologist\u003c/em\u003e \u003cstrong\u003e100\u003c/strong\u003e: 681\u0026ndash;690.\u003c/li\u003e\n \u003cli\u003eColhoun EA (1996). Application of Iversen\u0026rsquo;s glacial-interglacial cycle to interpretation of the late last glacial and Holocene vegetation history of western Tasmania. \u003cem\u003eQuaternary Science Reviews\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e: 557\u0026ndash;580.\u003c/li\u003e\n \u003cli\u003eColhoun EA, Benger SN, Fitzsimons SJ, Van De Geer G, Hill RS (1993). Quaternary organic deposit from Newton Creek valley, Western Tasmania. \u003cem\u003eAustralian Geographical Studies\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e: 26\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eColhoun EA, van de Geer G (1986). Holocene to middle last glaciation vegetation history at Tullabardine Dam, western Australia. \u003cstrong\u003e229\u003c/strong\u003e: 177\u0026ndash;207.\u003c/li\u003e\n \u003cli\u003eColhoun EA, van de Geer G, Fitzsimons SJ (1991). Late glacial and Holocene vegetation history at Governor Bog, King Valley, western Tasmania, Australia. \u003cem\u003eJournal of Quaternary Science\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e: 55\u0026ndash;66.\u003c/li\u003e\n \u003cli\u003eColhoun EA, Goede A (1979). The late Quaternary deposits of Blakes Opening and the middle Huon valley, Tasmania. \u003cem\u003ePhilosophical Transactions of the Royal Society of London B: Biological Sciences\u003c/em\u003e \u003cstrong\u003e286\u003c/strong\u003e: 371\u0026ndash;395.\u003c/li\u003e\n \u003cli\u003eColhoun EA, Pola JS, Barton CE, Heijnis H (1999). Late Pleistocene vegetation and climate history of Lake Selina, western Tasmania. \u003cem\u003eQuaternary International\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e: 5\u0026ndash;23.\u003c/li\u003e\n \u003cli\u003eColhoun EA, Shimeld PW (2012). Late-Quaternary vegetation history of Tasmania from pollen records. In: \u003cem\u003eTerra Australis\u003c/em\u003e, ANU E Press, Canberra, Australia, pp 297\u0026ndash;328.\u003c/li\u003e\n \u003cli\u003eCooley S, Fletcher M-S, Lis\u0026eacute;-Pronovost A, May J-H, Mariani M, Gadd P, \u003cem\u003eet al.\u003c/em\u003e (2024). Rainforest response to glacial terminations before and after human arrival in Lutruwita (Tasmania). \u003cem\u003eQuaternary Science Reviews\u003c/em\u003e \u003cstrong\u003e329\u003c/strong\u003e: 108572.\u003c/li\u003e\n \u003cli\u003eCorander J, Waldmann P, Sillanp\u0026auml;\u0026auml; MJ (2003). Bayesian analysis of genetic differentiation between populations. \u003cem\u003eGenetics\u003c/em\u003e \u003cstrong\u003e163\u003c/strong\u003e: 367\u0026ndash;374.\u003c/li\u003e\n \u003cli\u003eDe Deckker P, van der Kaars S, Macphail MK, Hope GS (2019). Land-sea correlations in the Australian region: 460 ka of changes recorded in a deep-sea core offshore Tasmania. Part 1: the pollen record. \u003cem\u003eAustralian Journal of Earth Sciences\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e: 1\u0026ndash;15.\u003c/li\u003e\n \u003cli\u003eDepartment of Natural Resources and Environment Tasmania, Tasmanian Government (2025). Ancient Huon pines confirmed intact following fire impact assessments.\u003c/li\u003e\n \u003cli\u003eEckenwalder JE (2009). \u003cem\u003eConifers of the world: the complete reference\u003c/em\u003e. Timber Press.\u003c/li\u003e\n \u003cli\u003eEl Mousadik A, Petit RJ (1996). High level of genetic differentiation for allelic richness among populations of the argan tree [\u003cem\u003eArgania spinosa\u003c/em\u003e (L.) Skeels] endemic to Morocco. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e: 832\u0026ndash;839.\u003c/li\u003e\n \u003cli\u003eExcoffier L, Hofer T, Foll M (2009). Detecting loci under selection in a hierarchically structured population. \u003cem\u003eHeredity\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e: 285\u0026ndash;298.\u003c/li\u003e\n \u003cli\u003eFarjon A, Page C (Eds.) (1999). \u003cem\u003eConifers. Status Survey and Conservation Action Plan.\u003c/em\u003e IUCN, Gland, Switzerland and Cambridge, UK.\u003c/li\u003e\n \u003cli\u003eFletcher M-S, Bowman DM, Whitlock C, Mariani M, Hopf F, Benson A, \u003cem\u003eet al.\u003c/em\u003e (2021). The influence of climatic change, fire and species invasion on a Tasmanian temperate rainforest system over the past 18,000 years. \u003cem\u003eQuaternary Science Reviews\u003c/em\u003e \u003cstrong\u003e260\u003c/strong\u003e: 106824.\u003c/li\u003e\n \u003cli\u003eFletcher M-S, Bowman D, Whitlock C, Mariani M, Stahle L (2018). The changing role of fire in conifer-dominated temperate rainforest through the last 14,000 years. \u003cem\u003eQuaternary Science Reviews\u003c/em\u003e \u003cstrong\u003e182\u003c/strong\u003e: 37\u0026ndash;47.\u003c/li\u003e\n \u003cli\u003eFletcher M, Romano A, Lis\u0026eacute;‐Pronovost A, Mariani M, Henriquez W, Gadd P, \u003cem\u003eet al.\u003c/em\u003e (2024). Reconciling 22,000 years of landscape openness in a renowned wilderness. \u003cem\u003eGeographical Research\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 503-525.\u003c/li\u003e\n \u003cli\u003eFletcher M, Thomas I (2007a). Holocene vegetation and climate change from near Lake Pedder, south‐west Tasmania, Australia. \u003cem\u003eJournal of Biogeography\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e: 665\u0026ndash;677.\u003c/li\u003e\n \u003cli\u003eFletcher MS, Thomas I (2007b). Modern pollen-vegetation relationships in western Tasmania, Australia. \u003cem\u003eReview of Palaeobotany and Palynology\u003c/em\u003e \u003cstrong\u003e146\u003c/strong\u003e: 146\u0026ndash;168.\u003c/li\u003e\n \u003cli\u003eFletcher MS, Thomas I (2010). The origin and temporal development of an ancient cultural landscape. \u003cem\u003eJournal of Biogeography\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e: 2183\u0026ndash;2196.\u003c/li\u003e\n \u003cli\u003eFletcher MS, Wolfe BB, Whitlock C, Pompeani DP, Heijnis H, Haberle SG, \u003cem\u003eet al.\u003c/em\u003e (2014). The legacy of mid-Holocene fire on a Tasmanian montane landscape. \u003cem\u003eJournal of Biogeography\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e: 476\u0026ndash;488.\u003c/li\u003e\n \u003cli\u003eFoll M, Gaggiotti O (2008). A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. \u003cem\u003eGenetics\u003c/em\u003e \u003cstrong\u003e180\u003c/strong\u003e: 977\u0026ndash;993.\u003c/li\u003e\n \u003cli\u003eGibson N (1988). A description of the Huon Pine (\u003cem\u003eLagarostrobus franklinii\u003c/em\u003e (Hook. F.) C. J. Quinn) forest of the Prince of Wales and King Billy Ranges. \u003cem\u003ePapers and proceedings of the royal society of Tasmania\u003c/em\u003e \u003cstrong\u003e122\u003c/strong\u003e: 127\u0026ndash;133.\u003c/li\u003e\n \u003cli\u003eGibson N, Davies J, Brown M (1991). The ecology of Lagarostrobos franklinii (Hook. f.) Quinn (Podocarpaceae) in Tasmania. 1. Distribution, floristics and environmental correlates. \u003cem\u003eAustralian journal of ecology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e: 215\u0026ndash;222.\u003c/li\u003e\n \u003cli\u003eHaasl RJ, Payseur BA (2011). Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites. \u003cem\u003eHeredity\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e: 158\u0026ndash;171.\u003c/li\u003e\n \u003cli\u003eHedrick PW (2005). A standardized genetic differentiation measure. \u003cem\u003eEvolution\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e: 1633\u0026ndash;1638.\u003c/li\u003e\n \u003cli\u003eHenr\u0026iacute;quez WI, Fletcher M-S, Romano A (2023). Vegetation, fire and climate history in central-western Tasmania (41\u0026deg;S), Australia, over the last\u0026sim;\u0026nbsp;21,000 years. \u003cem\u003eQuaternary Science Reviews\u003c/em\u003e \u003cstrong\u003e318\u003c/strong\u003e: 108315.\u003c/li\u003e\n \u003cli\u003eHill RS, MacPhail MK (1985). A Fossil Flora from rafted Plio-Pleistocene mudstones at Regatta Point, Tasmania. \u003cem\u003eAustralian Journal of Botany\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e: 497\u0026ndash;517.\u003c/li\u003e\n \u003cli\u003eHolbeck M (2022). Treasure in the trees: ancient stand of Huon pines \u0026lsquo;discovered\u0026rsquo;. \u003cem\u003eAustralian Geographic\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eHolm S (1979). A simple sequentially rejective multiple test procedure. \u003cem\u003eScandinavian journal of statistics\u003c/em\u003e: 65\u0026ndash;70.\u003c/li\u003e\n \u003cli\u003eHolz A, Wood SW, Veblen TT, Bowman DMJS (2015). Effects of high‐severity fire drove the population collapse of the subalpine Tasmanian endemic conifer \u003cem\u003eAthrotaxis cupressoides\u003c/em\u003e. \u003cem\u003eGlobal change biology\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e: 445\u0026ndash;458.\u003c/li\u003e\n \u003cli\u003eHolz A, Wood SW, Ward C, Veblen TT, Bowman DMJS (2020). Population collapse and retreat to fire refugia of the Tasmanian endemic conifer \u003cem\u003eAthrotaxis selaginoides\u003c/em\u003e following the transition from Aboriginal to European fire management. \u003cem\u003eGlobal Change Biology\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e: 3108\u0026ndash;3121.\u003c/li\u003e\n \u003cli\u003eHuson DH, Bryant D (2006). Application of phylogenetic networks in evolutionary studies. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e: 254\u0026ndash;267.\u003c/li\u003e\n \u003cli\u003eJackson W (1999). The Tasmanian legacy of man and fire. \u003cem\u003ePapers and proceedings of the Royal Society of Tasmania\u003c/em\u003e \u003cstrong\u003e133\u003c/strong\u003e: 1\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eJeffreys H (1998). \u003cem\u003eThe Theory of Probability\u003c/em\u003e. OUP Oxford.\u003c/li\u003e\n \u003cli\u003eJombart T (2008). adegenet: a R package for the multivariate analysis of genetic markers. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e: 1403\u0026ndash;1405.\u003c/li\u003e\n \u003cli\u003eJordan GJ, Carpenter RJ, Hill RS (1991). Late Pleistocene vegetation and climate near Melaleuca Inlet, south-western Tasmania. \u003cem\u003eAustralian Journal of Botany\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e: 315\u0026ndash;333.\u003c/li\u003e\n \u003cli\u003eJost LOU (2008). GST and its relatives do not measure differentiation. \u003cem\u003eMolecular Ecology\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e: 4015\u0026ndash;4026.\u003c/li\u003e\n \u003cli\u003eKalinowski ST (2005). hp‐rare 1.0: a computer program for performing rarefaction on measures of allelic richness. \u003cem\u003eMolecular Ecology Notes\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e: 187\u0026ndash;189.\u003c/li\u003e\n \u003cli\u003eKerr GJ, McDermott H (1999). \u003cem\u003eThe Huon Pine Story: The history of harvest and use of a unique timber\u003c/em\u003e. Mainsail Books.\u003c/li\u003e\n \u003cli\u003eKhan R, Hill RS, Liu J, Biffin E (2023). Diversity, distribution, systematics and conservation status of Podocarpaceae. \u003cem\u003ePlants\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e: 1171.\u003c/li\u003e\n \u003cli\u003eKirkpatrick JB, Styger JK, Marsden-Smedley JB (2018). Impact of changes in lightning fire incidence on the values of the Tasmanian wilderness world heritage area. In: Vol 152, pp 27\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eLangella O (2011). Populations 1.2. 31 CNRS UPR9034.\u003c/li\u003e\n \u003cli\u003eLedig FT (1992). Human impacts on genetic diversity in forest ecosystems. \u003cem\u003eOikos\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e: 87.\u003c/li\u003e\n \u003cli\u003eL\u0026oacute;pez‐Cortegano E, P\u0026eacute;rez‐Figueroa A, Caballero A (2019). metapop2: Re‐implementation of software for the analysis and management of subdivided populations using gene and allelic diversity. \u003cem\u003eMolecular Ecology Resources\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e: 1095\u0026ndash;1100.\u003c/li\u003e\n \u003cli\u003eLotterhos KE, Whitlock MC (2014). Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. \u003cem\u003eMolecular Ecology\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e: 2178\u0026ndash;2192.\u003c/li\u003e\n \u003cli\u003eMacphail MK (1981). A history of fire in the forest. \u003cem\u003eFire and Forest Management in Tasmania: Tasmanian Conservation Trust, Hobart\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e: 22.\u003c/li\u003e\n \u003cli\u003eMacphail MK, Colhoun EA (1985). Late last glacial vegetation, climates and fire activity in southwest Tasmania. \u003cem\u003eSearch\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e: 43\u0026ndash;45.\u003c/li\u003e\n \u003cli\u003eMarthick JR, Larcombe MJ, Worth JRP (2020). Development of EST microsatellite markers for the Tasmanian palaeoendemic conifer \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e ( Hook . f .) Quinn ( Podocarpaceae ). \u003cem\u003eSilvae Genetica\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e: 1\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003eMarthick JR, Phipps AJ, Jordan GJ, Takahashi D, Suyama Y, Worth JRP (submitted). Stand structure and genetic diversity in one of the world\u0026rsquo;s oldest conifer clones, \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e at Mt Read, western Tasmania.\u003c/li\u003e\n \u003cli\u003eMillington RJ, Jones R, Brown D, Vernon B (1979). \u003cem\u003eHuon pine - endangered? Environmental Studies Occasional Paper\u003c/em\u003e. University of Tasmania, Hobart.\u003c/li\u003e\n \u003cli\u003eMorales-Hidalgo D, Oswalt SN, Somanathan E (2015). Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. \u003cem\u003eForest Ecology and Management\u003c/em\u003e \u003cstrong\u003e352\u003c/strong\u003e: 68\u0026ndash;77.\u003c/li\u003e\n \u003cli\u003eMosseler A, Major J, Rajora O (2003). Old-growth red spruce forests as reservoirs of genetic diversity and reproductive fitness. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e: 931\u0026ndash;937.\u003c/li\u003e\n \u003cli\u003eMunoz I, Henriques D, Jara L, Johnston JS, Ch\u0026aacute;vez‐Galarza J, De La Rua P, \u003cem\u003eet al.\u003c/em\u003e (2017). SNP s selected by information content outperform randomly selected microsatellite loci for delineating genetic identification and introgression in the endangered dark European honeybee (\u003cem\u003eApis mellifera mellifera\u003c/em\u003e). \u003cem\u003eMolecular Ecology Resources\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e: 783\u0026ndash;795.\u003c/li\u003e\n \u003cli\u003eNei M, Tajima F, Tateno Y (1983). Accuracy of estimated phylogenetic trees from molecular data. \u003cem\u003eJournal of Molecular Evolution\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e: 153\u0026ndash;170.\u003c/li\u003e\n \u003cli\u003eParadis E (2010). pegas: an R package for population genetics with an integrated\u0026ndash;modular approach. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e: 419\u0026ndash;420.\u003c/li\u003e\n \u003cli\u003eParviainen J (2005). Virgin and natural forests in the temperate zone of Europe. \u003cem\u003eForest Snow and Landscape Research\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e: 9\u0026ndash;18.\u003c/li\u003e\n \u003cli\u003ePeakall ROD, Smouse PE (2006). GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. \u003cem\u003eMolecular Ecology Resources\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e: 288\u0026ndash;295.\u003c/li\u003e\n \u003cli\u003ePelz K, Hayward G, Gray A, Berryman E, Woodall C, Nathanson A, \u003cem\u003eet al.\u003c/em\u003e (2023). Quantifying old-growth forest of United States Forest Service public lands. \u003cem\u003eForest Ecology and Management\u003c/em\u003e \u003cstrong\u003e549\u003c/strong\u003e: 121437.\u003c/li\u003e\n \u003cli\u003ePeterson MJ (1990). \u003cem\u003eDistribution and conservation of Huon pine\u003c/em\u003e. Forestry Commission: Hobart, Tasmania.\u003c/li\u003e\n \u003cli\u003ePetit RJ, Mousadik AE, Pons O (1998). Identifying populations for conservation on the basis of genetic markers. \u003cem\u003eConservation Biology\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e: 844\u0026ndash;855.\u003c/li\u003e\n \u003cli\u003ePotapov P, Yaroshenko A, Turubanova S, Dubinin M, Laestadius L, Thies C, \u003cem\u003eet al.\u003c/em\u003e (2008). Mapping the world\u0026rsquo;s intact forest landscapes by remote sensing. \u003cem\u003eEcology and Society\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003ePurcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, \u003cem\u003eet al.\u003c/em\u003e (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. \u003cem\u003eThe American Journal of Human Genetics\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e: 559\u0026ndash;575.\u003c/li\u003e\n \u003cli\u003eR Core Team (2022). R Core team 2021 R: A language and environment for statistical computing. \u003cem\u003eR: A language and environment for statistical computing\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eRaymond M, Rousset F (1995). GENEPOP (version 1.2): population genetics software for Windows and Linux. \u003cem\u003eMolecular Ecology Resources\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e: 103\u0026ndash;106.\u003c/li\u003e\n \u003cli\u003eRead J, Busby J (1990). Comparative responses to temperature of the major canopy species of Tasmanian cool temperate rain-forest and their ecological significance II. Net photosynthesis and climate analysis. \u003cem\u003eAustralian Journal of Botany\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e: 185\u0026ndash;205.\u003c/li\u003e\n \u003cli\u003eRochette NC, Rivera-Col\u0026oacute;n AG, Catchen JM (2019). Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. \u003cem\u003eMolecular Ecology\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e: 4737\u0026ndash;4754.\u003c/li\u003e\n \u003cli\u003eRoque RH, Sebbenn AM, Boshier DH, Filho AF, Tambarussi EV (2023). Logging affects genetic diversity parameters in an \u003cem\u003eAraucaria angustifolia\u003c/em\u003e population: an endangered species in southern Brazil. \u003cem\u003eForests\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e: 1046.\u003c/li\u003e\n \u003cli\u003eSchaberg PG, DeHayes DH, Hawley GJ, Nijensohn SE (2008). Anthropogenic alterations of genetic diversity within tree populations: Implications for forest ecosystem resilience. \u003cem\u003eForest ecology and management\u003c/em\u003e \u003cstrong\u003e256\u003c/strong\u003e: 855\u0026ndash;862.\u003c/li\u003e\n \u003cli\u003eShapcott A (1991). Dispersal and establishment of Huon pine (\u003cem\u003eLagarostrobos franklinii\u003c/em\u003e). \u003cem\u003ePapers and Proceedings of the Royal Society of Tasmania\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e: 17\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eShapcott A (1993). The population genetics of two temperate rainforest trees, \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e (Hook f.) Quinn (Huon pine), and \u003cem\u003eAtherosperma moschatum\u003c/em\u003e Labill. (Sassafras).\u003c/li\u003e\n \u003cli\u003eShapcott A (1997). Population genetics of the long-lived Huon pine \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e: An endemic Tasmanian temperate rainforest tree. \u003cem\u003eBiological Conservation\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e: 169\u0026ndash;179.\u003c/li\u003e\n \u003cli\u003eStyger J, Marsden-Smedley J, Kirkpatrick J (2018). Changes in lightning fire incidence in the Tasmanian Wilderness World Heritage Area, 1980\u0026ndash;2016. \u003cem\u003eFire\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e: 38.\u003c/li\u003e\n \u003cli\u003eSunde J, Yıldırım Y, Tibblin P, Forsman A (2020). Comparing the performance of microsatellites and RADseq in population genetic studies: Analysis of data for pike (\u003cem\u003eEsox lucius\u003c/em\u003e) and a synthesis of previous studies. \u003cem\u003eFrontiers in Genetics\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e: 218.\u003c/li\u003e\n \u003cli\u003eSuyama Y, Hirota SK, Matsuo A, Tsunamoto Y, Mitsuyuki C, Shimura A, \u003cem\u003eet al.\u003c/em\u003e (2022). \u003cem\u003eComplementary combination of multiplex high‐throughput DNA sequencing for molecular phylogeny\u003c/em\u003e. Wiley Online Library.\u003c/li\u003e\n \u003cli\u003eSuyama Y, Matsuki Y (2015). MIG-seq: An effective PCR-based method for genome-wide single-nucleotide polymorphism genotyping using the next-generation sequencing platform. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e: 1\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eTasmanian Planning Commission (2024). \u003cem\u003eTasmanian 2024 State of the Environment Report\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eThe jamovi project (2025). jamovi (Version 2.6).\u003c/li\u003e\n \u003cli\u003eVan De Geer G, Fitzsimons SJ, Colhoun EA (1991). Holocene vegetation history from King River railway bridge, western Tasmania. \u003cem\u003ePapers and Proceedings - Royal Society of Tasmania\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e: 73\u0026ndash;77.\u003c/li\u003e\n \u003cli\u003eVan De Geer G, Heusser LE, Lynch‐Stieglitz J, Charles CD (1994). Paleoenvironments of Tasmania inferred from a 5\u0026ndash;75 ka marine pollen record. \u003cem\u003ePalynology\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e: 33\u0026ndash;40.\u003c/li\u003e\n \u003cli\u003eWidiyatno, Indrioko S, Na\u0026rsquo;iem M, Uchiyama K, Numata S, Ohtani M, \u003cem\u003eet al.\u003c/em\u003e (2016). Effects of different silvicultural systems on the genetic diversity of \u003cem\u003eShorea parvifolia\u003c/em\u003e populations in the tropical rainforest of Southeast Asia. \u003cem\u003eTree Genetics \u0026amp; Genomes\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e: 1\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eWinter DJ (2012). MMOD: an R library for the calculation of population differentiation statistics. \u003cem\u003eMolecular Ecology Resources\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e: 1158\u0026ndash;1160.\u003c/li\u003e\n \u003cli\u003eWorth JRP, Jordan GJ, Marthick JR, Sakaguchi S, Colhoun EA, Williamson GJ, \u003cem\u003eet al.\u003c/em\u003e (2017). Fire is a major driver of patterns of genetic diversity in two co‐occurring Tasmanian palaeoendemic conifers. \u003cem\u003eJournal of Biogeography\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e: 1254\u0026ndash;1267.\u003c/li\u003e\n \u003cli\u003eWorth JR, Marthick JR, Harrison PA, Sakaguchi S, Jordan GJ (2021). The palaeoendemic conifer \u003cem\u003ePherosphaera hookeriana\u003c/em\u003e (Podocarpaceae) exhibits high genetic diversity despite Quaternary range contraction and post glacial bottlenecking. \u003cem\u003eConservation Genetics\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e: 307\u0026ndash;321.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Details of the 33 populations of \u003cem\u003eLagarostrobos franklinii\u003c/em\u003e sampled including geographical location, stand type (either occurring along rivers/creeks, at the edge of lakes or non-riverine), the water catchment named after the largest river(s) (for the non-riverine stand this corresponded with catchment of the closest creeks), disturbance history (i.e. disturbed by fire/logging or primary) and the number of samples used for nuclear SSR and MIG-seq genotyping.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"859\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eElevation (masl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eStand Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eCatchment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eDisturbance history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003en for nSSR*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003en for MIG-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eYellow Ck.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-41.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ePieman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eHarman Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-41.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ePieman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eWilsons Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-41.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ePieman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e27 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eStanley Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-41.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ePieman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003ePieman Rv. Corrina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-41.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ePieman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e28 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eNewell Ck.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eKing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e31 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eTeepookana Plateau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNon-riverine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eKing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e25 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eBird Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eBird\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLoddon- Franklin conflu.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e19 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLake Vera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e32 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLake Marilyn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e31 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eBuckley\u0026apos;s Chance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNon-riverine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e28 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eErebus- Jane conflu.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eNewlands Cascade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e20 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eJane Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e30 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eKutikina Cave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eFranklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e10 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eGordon Rv. - 15km upstream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eGordon/ Denison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e31 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eSir John Falls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eGordon/ Denison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eDenison- Gordon conflu.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eGordon/ Denison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e18 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eDenison Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eGordon/ Denison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eSerpentine Gorge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eGordon/ Denison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eGilbert Leitch HPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eGordon/ Denison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eDavey Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e145.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eDavey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eBadgers Ck.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eDavey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e13 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eCrossing Rv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eDavey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eCondominium Ck.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLake Judd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-42.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e28 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eScotts Peak Dam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e29 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eHuon Rv. Gorge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e26 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eFarmhouse Ck.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e30 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eRiveaux Ck.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e19 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eTahune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e33 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eSouthwood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-43.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e146.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRiver/ Creek\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eHuon/ Picton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003edisturbed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e30 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Denotes the number of clonal samples excluded from the nSSR analyses \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Average genetic diversity statistics at the catchment level based on the nuclear SSR dataset.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCatchment*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003ePolymorphic loci (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eNe\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eHo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003euHe\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003eFis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eAr\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cem\u003ePAr\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003ePieman (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eKing (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eBird (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eFranklin (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eGordon/Denison (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eDavey (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eHuon/Picton (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e98.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eOverall Average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e99.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*The numbers in brackets indicate the number of populations sampled in each catchment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Average genetic diversity statistics at the catchment level based on the MIG-seq SNP dataset.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCatchment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;Polymorphic loci (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003eNe\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eHo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eHe\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003euHe\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eFis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003eAr\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003ePAr\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003ePieman (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e39.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eKing (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e41.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eBird (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e45.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eFranklin (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e47.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eGordon/Denison (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e50.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eDavey (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e48.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eHuon/Picton \u0026nbsp;(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e44.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eOverall Average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e45.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"conservation priority, core vs peripheral populations, cryptic genetic divergence, human impact, MIG-seq, nuclear SSRs ","lastPublishedDoi":"10.21203/rs.3.rs-6792156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6792156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The impact of past anthropogenic disturbance on the amount and distribution of genetic diversity is a key factor in determining the resilience of tree species to environmental change. This is particularly the case for narrowly distributed species where this disturbance has impacted most of the species’ range. Here we examine the legacy of post-colonial logging and fire on patterns of genetic diversity in the Tasmanian palaeoendemic conifer Lagarostrobos franklinii (Podocarpaceae), a fire sensitive and slow growing rainforest tree valued for its durable timber. Thirty-three populations (12 of which represent primary stands) from across the species range were genotyped using 8 nuclear SSRs (871 samples) and MIG-seq-based single nucleotide polymorphisms (254 samples). Genetic differentiation was relatively high for conifers (Fst of 0.113 and 0.143 for nuclear SSR and MIG-seq, respectively) with the most diverged populations near the species northern and southern range limits and cryptic divergence between populations geographically close but in differing river catchments likely reflecting postglacial dispersal from distinct Last Glacial refugia and low levels of gene flow. Population level genetic diversity was greatest in the core of the range with no significant correlation with the history of post-colonial human disturbance (i.e. primary vs. non primary stands) and, unexpectedly, given the greater impact of logging at lower elevations, a significant decline in allelic richness with elevation. Overall, this study shows that L. franklinii has been resilient to past timber exploitation and uncovers previously undetected genetic patterns that will help guide the conservation of this important conifer into the future.","manuscriptTitle":"Last Glacial and Holocene dynamics override post-colonial disturbance in shaping genetic diversity of a heavily exploited palaeoendemic conifer, Lagarostrobos franklinii","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 12:07:42","doi":"10.21203/rs.3.rs-6792156/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-06-27T17:19:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-26T12:16:01+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-16T18:50:37+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-13T08:41:40+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-12T06:50:27+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-06-11T13:43:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-31T16:47:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Heredity","date":"2025-05-31T16:47:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"heredity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"hdy","sideBox":"Learn more about [Heredity](http://www.nature.com/hdy/)","snPcode":"41437","submissionUrl":"https://mts-hdy.nature.com/cgi-bin/main.plex","title":"Heredity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"55e97ebc-1f7d-4104-843f-f8783c28cced","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49985295,"name":"Biological sciences/Genetics"},{"id":49985296,"name":"Biological sciences/Evolution/Population genetics"},{"id":49985297,"name":"Biological sciences/Ecology/Conservation biology"}],"tags":[],"updatedAt":"2025-10-15T07:17:36+00:00","versionOfRecord":{"articleIdentity":"rs-6792156","link":"https://doi.org/10.1038/s41437-025-00798-2","journal":{"identity":"heredity","isVorOnly":false,"title":"Heredity"},"publishedOn":"2025-10-14 04:00:00","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-06-16 12:07:42","video":"","vorDoi":"10.1038/s41437-025-00798-2","vorDoiUrl":"https://doi.org/10.1038/s41437-025-00798-2","workflowStages":[]},"version":"v1","identity":"rs-6792156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6792156","identity":"rs-6792156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}