Genetic Lag in a Demographically Recovering Carnivore: The Case of the British Pine Marten (Martes martes)

Genetic Lag in a Demographically Recovering Carnivore: The Case of the British Pine Marten (Martes martes) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic Lag in a Demographically Recovering Carnivore: The Case of the British Pine Marten (Martes martes) Catherine O’Reilly, Emma Sheehy, Jenny MacPherson, Johnny Birks, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3997852/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Conservation Genetics → Version 1 posted 9 You are reading this latest preprint version Abstract We investigated the genetic diversity of the contemporary Scottish pine marten population using neutral microsatellite markers, sampling 206 individuals across an area of almost 32,000 km 2 . Our results revealed that the genetic diversity in the Scottish population is modest with the levels of observed and expected heterozygosity ranging from the Highlands (H o 0.52, H e 0.55) to the Cairngorms (H o 0.44, H e 0.42), and the number of alleles ranged from 3.3 in the Highlands and Central to 2.3 in Dumfries and Galloway, but there were high levels of genetic admixture across the country, some of which may be attributed to natural demographic recovery from previously isolated refuges, and unofficial translocations have also influenced the genetic mixing evident in the population today. Genetic sub structuring, resulting in the Wahlund effect, complicated evaluations of diversity, effective population size, and bottlenecks, and commonly used linkage disequilibrium methods for estimating effective population size yielded improbably low figures. A less commonly used method relying on sibship proved more resilient to the effects of genetic sub structuring, but still yielded estimates under 200, below the viability threshold for long-term population survival. Despite demographic expansion, genetic recovery lagged, suggesting the need for increased gene flow through wildlife corridors. Conservation Management Restoration Population Genetics Microsatellites Martes martes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Pine martens ( Martes martes ), a medium sized mustelid, on the islands of mainland Britain and Ireland have endured what Birks ( 2020 ) described as a "horrendous history" since their heyday in the British Mesolithic and Irish Neolithic periods (Montgomery et al., 2014 ). Since then, their populations declined drastically, resulting in widescale regional extinctions in both countries due to human impacts. Starting in the Neolithic in Britain, the near-complete clearance of woodland - the optimal pine marten habitat - to just five percent cover in the early 1900s (Forestry Commission, 2003 ), and its replacement with open agricultural land, was the main impact. This was exacerbated by additional factors such as fur trapping, killing martens as perceived vermin, and gamekeeping and hunting (Tapper, 1992 ; Croose et al., 2014 ; Birks 2020 ; O’Reilly et al., 2021 ). However, in certain areas of Britain and Ireland, pockets of pine martens managed to survive (Birks, 2020 ; O'Reilly et al. , 2021). In Ireland, since reaching its nadir in the early 20th century, the pine marten population has significantly expanded, aided by legal protection, and increased tree cover (O’Mahony et al., 2017a &b; Lawton et al., 2020 ). Similarly, in Britain, the species reached its lowest point in the early 20th century. It is thought to have primarily contracted to the Northwest Highlands of Scotland, an area characterised by rocky terrain, fragmented woodlands, and minimal human interference. Additionally, smaller, isolated populations are assumed to have persisted in upland areas of England and Wales (Langley & Yalden, 1977). Since the 20th century, the Scottish population has experienced significant recovery due to reduced trapping, thanks to legal protection, increased forest cover, and translocations (Shaw & Livingstone 1992 ; Croose et al., 2014 ). Twelve pine martens (comprising six males and six females) were relocated from western Inverness-shire, to Galloway Forest Park in southwest Scotland between 1980 and 1981 (Shaw & Livingstone, 1992 ). More recent relocations have also occurred, including 14 animals, rescued as orphans in the Highlands, later unofficially reintroduced into the Scottish Borders following rehabilitation (Croose et al., 2014 ). Reliable sources indicate an ongoing programme of unofficial releases in southern Scotland, involving young pine martens rescued as orphans from various parts of Scotland (J. Birks & J. Martin, Pers. obs.). The most recent population estimate for pine martens in Britain is approximately 3,700, with a 95% confidence interval ranging from 1,600 to 8,900 (Mathews & Harrower, 2020 ), an estimate closely aligned with the one provided by Harris et al., ( 1995 ), though derived through a different calculation method. Given the demographic expansion observed in Scotland, Mathews & Harrower ( 2020 ) suggested that the actual number might trend towards the higher end of their estimate. Consequently, certain regions within the Scottish Highlands were assessed as stable enough to permit the sustainable removal and translocation of animals for population reinforcement and restoration projects in Wales and England (MacPherson et al., 2014 ). From 2015 to 2017, 51 pine martens were taken to Wales (McNicol et al., 2020 ). Similarly, between 2019 and 2021, a reintroduction to the Forest of Dean in Gloucestershire received pine martens taken from Scotland, starting with a population of 35 individuals (E. Croose & J. MacPherson, Pers. obs.). There is growing evidence to suggest that the restoration of native vertebrates can contribute to landscape-scale biological control of established invasive vertebrates (Twining et al., 2022 ). Restoration efforts are often driven by community desires to restore native species, curb biodiversity loss, and reap the ecosystem benefits of pine martens in potentially controlling invasive species like the North American grey squirrel ( Sciurus carolinensis ) as culling efforts are not proving successful in the long-term (Synnott et al., 2023 ). Furthermore, pine martens, at healthy population densities, may aid the recovery of other native species like the red squirrel ( Sciurus vulgaris ) (Sheehy & Lawton, 2014 ; Sheehy et al., 2018 ; Twining et al., 2020 ). The mechanisms behind these interactions are not yet fully understood, but molecular tools have confirmed predation of grey squirrels by pine martens (O’Meara et al., 2014 ; Sheehy et al., 2014 ). In Britain and Ireland, where large carnivores have become extinct, restoration efforts for remaining species are hindered by the lack of natural inward migration from the continent, essential for maintaining genetic diversity. O'Reilly et al. , (2021) reported moderate genetic diversity within the recovering Irish pine marten population (average He and H 0 0.55 and 4.3 alleles), alongside evidence of genetic bottlenecks and a modest effective population size. Similarly, despite the demographic resurgence of Britain's otter ( Lutra lutra ) population, genetic studies, including Thomas et al., ( 2022 ), reveal a potential genetic lag, a term used to broadly explain how a population’s genetic recovery may not keep pace with a demographic expansion. Analysis of 90 otter samples from 2014 showed average expected heterozygosity (H e ) between 0.45 and 0.67, observed heterozygosity (H o ) between 0.37 and 0.55, and allele counts between 3 and 4.2. The 1994 data, based on only 28 samples, exhibited H e values from 0.48 to 0.66, H o from 0.44 to 0.7, and allele counts from 1.5 to 3.2, suggesting that demographic recovery does not necessarily lead to genetic recovery. In the development of conservation strategies for recovering species such as the pine marten and otter, it is important to take genetic factors into account, especially when concerns about genetic diversity remain despite population growth. In contrast to the genetic diversity found in recovering native species, many invasive species appear to be genetically thriving, most likely due to divergent and mixed origins of the species, making some of the invasive populations more diverse than their native counterparts. The North American mink ( Neovison vison ), imported into Europe originally for fur farming, has extensive levels of genetic diversity in Europe, with 31 different mtDNA haplotypes found within Poland alone (Zalewski et al. , 2011). A genetic study in Scotland showed differing levels of genetic diversity according to population and location. The largest sample containing 273 individuals had an average H e of 0.62 and H o of 0.61, and an average of 7.2 alleles, but these numbers declined in other sampling areas with smaller sample sizes e.g. one site with 30 animals had a H e of 0.4 and H o of 0.39 and 3.2 alleles (Fraser et al. , 2013). The invasive North American grey squirrel in North Wales also had high genetic diversity, with H e and H o averaging 0.56 and 0.35 respectively, and the number of alleles averaging 6.7 (Synnott et al., 2023 ). Despite regular culling of the species in North Wales, over a 10 year period, the population was not found to have experienced any significant decreases in diversity, due to the inward migration of new animals with divergent genetic origins in North America, encompassing a total of six distinct mtDNA haplotypes within a relatively small geographic area. While there are many factors contributing to the success of invasive species, the examples provided illustrate how high levels of genetic diversity, resulting from divergent and mixed origins, can complicate efforts to manage or exterminate these species, a contrast to the challenges faced by some native species, which, in certain cases, suffer from reduced genetic viability. Despite our general understanding that higher genetic diversity can lead to greater population resilience (Frankham et al., 2014 ), studies such as those of the otter in Britain and the pine marten in Ireland have shown that despite spatial and demographic recovery, genetics can sometimes lag behind this demographic recovery. While obvious population viability problems may not exist now, it is possible that issues could arise in the future due to reduced viability and evolutionary potential. Thomas et al., ( 2022 ), noted that few studies exist which can provide empirical evidence of the time needed for genetic recovery of a species following demographic recovery, or indeed if other anthropogenic factors impede expected increases in genetic diversity, in parallel with demographic recovery (Hoban et al., 2021 ). This study aims to provide genetic insights to enhance ongoing translocation efforts, maximizing their effectiveness, and to facilitate adaptive decision-making by supplying data crucial for protecting the genetic integrity and viability of the British pine marten. This approach is expected to align with and support sustainable, long-term conservation and management practices in accordance with the Pine Marten Recovery Programme (MacPherson & Wright, 2021 ). Materials and Methods Sample collection A total of 206 samples collected over a period spanning three years between 2014 and 2017 were used in this study and were generated as part of a number of studies and reports including Sheehy et al., 2018 (n = 94), collected between 2015 and 2016; the Game and Wildlife Conservation Trust (GWCT) (n = 18), collected in 2015, The Vincent Wildlife Trust (n = 59), collected between 2015 and 2016 and a study in Dumfries and Galloway (Croose et al., 2019 ) (n = 35), collected in 2014 and 2017. Most of the samples were hair samples collected via non-invasive hair-tube studies using the method described in Mullins et al., ( 2010 ) or squirrel feeders with glue patches (Sheehy et al., 2018 ), and a total of seven opportunistically collected roadkill samples from Scotland were also included which did not have defined grid references. The samples were not collected for the purpose of a population genetics study. However, they eventually offered an opportunity to be compiled for that purpose, but due to the nature of the sampling process, the distribution of samples is uneven, representing ad-hoc 'sampling zones' in Fig. 1 . The distribution of 198 grid referenced samples used in this study is mapped in Fig. 1 . Individual sample locations were mapped using the following programmes and libraries in Python 3 (Van Rossum & Drake, 2009): Rasterio (Gillies et al. , 2019); Matplotlib (Hunter 2007 ); and GeoPandas (Van den Bossche et al., 2023 ). We used the 'Scotland’s Wildness – Ruggedness' layer for visualisation to showcase the natural ruggedness of Scotland's landscape. This layer is made available by Scottish Natural Heritage (SNH) and is accessible under the OS Open Data licence. It relies on Ordnance Survey data © Crown copyright to accurately represent the rugged or physically challenging terrain. The layer's documentation can be found on the Spatialdata.Gov.Scot Metadata Portal, a resource managed by the Scottish Government and Astun Technology (2019), and is licensed under the Open Government Licence www.nationalarchives.gov.uk/doc/open-government-licence . To calculate the size of the sampling area, a bounding box was used, by determining the maximum and minimum X and Y values, creating a box around the data, and calculating the size of the area. To provide context of geographic positioning of Scotland in relation to Britain, Ireland and the north of France, a map was made with Natural Earth, a provider of free vector and raster map data www.naturalearthdata.com . Molecular Analysis DNA was extracted from samples and confirmed to species via qPCR using the methods outlined in O’Reilly et al., ( 2008 ); Mullins et al., ( 2010 )d Reilly et al., ( 2021 ). Samples were genotyped in triplicate at 12 microsatellite loci as per O’Reilly et al., ( 2021 ) and Croose et al., ( 2016 ) using the following markers: 12 microsatellite markers: Mel1, Ma2, Gg7, Mar21, Mvis1341 Mar53, Mar08, Mel105, Ma08, Mar64, Mer041 and Mvis075 (Davis & Strobeck, 1998 ; Carpenter et al., 2003 ; Fleming et al., 1999 ; Natali, 2010; O’Reilly et al., 2021 ). These markers were chosen for their high amplifiability in low-quality DNA samples, such as hair, and their proven efficacy in identifying unique individuals. Additionally, they were selected for their highly polymorphic nature, making them particularly suitable for assessing genetic diversity. This selection ensures that the most variable markers available are utilised, thereby providing a comprehensive understanding of the population's genetic diversity, but this also means that the markers are specifically chosen for characteristics that may not be uniformly distributed across all potential genetic markers in the population. Data analysis The number of alleles (A) and observed and expected heterozygosities (H o and H e ) were calculated via GENALEX v.6.5b (Peakall & Smouse, 2006 ). The inbreeding coefficient (F is ) was calculated using FSTAT v.2.9.3 (Goudet, 1995 ). The significance levels were determined by randomising the alleles within the population, using 10,000 permutations, and then comparing these with the observed data, an approach used to assess the presence of deviations from the Hardy–Weinberg equilibrium. Tests for linkage disequilibrium were performed between pairs of loci using FSTAT v.2.9.3 (Goudet, 1995 ). Calculation of effective population size and detection of genetic bottlenecks The programme NeEstimator v.2 (Do et al., 2014 ) was used to calculate the effective population size (Ne) and associated 95% confidence intervals using the linkage disequilibrium method developed by Waples & Do (2008) at two levels of lowest allele frequency (0.02, 0.05). Effective population size was also calculated using the sibship assignment method using the programme COLONY v2.0.7.0 (Jones & Wang 2010 ). The program facilitates analysis by assuming a range of potential mating systems, including polygamous and monogamous behaviours in both males and females. Given that mustelids are known for their flexible mating systems—for example, Mustela putorius exhibits polygyny (Lodé, 2001), and pine martens have a biological mechanism of delayed implantation that increases their opportunity to mate with multiple males (Yamaguchi et al. , 2004), we attempted analyses with configurations of both sexes being polygamous, monogamous, as well as with male-only polygamy and female monogamy. This approach is particularly relevant since a strictly polygamous mating system is known to reduce effective population size estimates (Wang et al. , 2013), and mustelids have an ability to avoid this. We selected a long-length run with five replicates to obtain random and non-random estimated effective population sizes with 95% confidence intervals. The program BOTTLENECK v.1.2.02 (Piry et al., 1999 ) was used to detect whether signatures of a genetic bottleneck were present in the Scottish pine marten population. Genetic bottlenecks are detected when there is an excess of heterozygosity in comparison to the number of alleles. The method implemented in the programme relies on the theory that at mutation-drift equilibrium (i.e. the effective size of the population has remained stable in the past), it is equally likely that a locus will exhibit either a heterozygosity excess or deficit. To test if the population contained a significant excess of heterozygosity, we used the three tests available: the "sign test", a "standardized differences test", and a "Wilcoxon sign-rank test", and applied three different models: Infinite Allele Model (IAM), Two Phase Model (TPM) and Stepwise Mutation Model (SMM). For the TPM model we applied the following settings: 80% single-step mutations, a variance among multiple steps of 12, and 5000 iterations. We used a descriptor of the allele frequency distribution ("mode-shift" indicator) which discriminates many bottlenecked populations from stable populations (Luikart et al., 1998 ). The M-ratio test (Garza & Williamson, 2001 ) was used to investigate whether a potential reduction in population size had occurred. If a reduction has occurred in the population size, the number of alleles (k) is expected to decline faster than the range in allele size (r) as most of the alleles that are lost from the population occur within the range, rather than at the edges resulting in a lower M-ratio (k/r). The M-ratio will be ≥ 0.8 if a population has not experienced a reduction in population size, while a value < 0.7 indicates that a reduction in population size has occurred. Genetic Differentiation To initially investigate the presence of genetic structure, a principal coordinate analysis (PCoA) was generated in GENALEX and visualised using Pandas (McKinney 2010 ) and Matplotlib (Hunter 2007 ) in Python 3 (Van Rossum & Drake, 2009). To help visualise the samples for genetic differentiation, it was categorised into the six sampling zones defined in Fig. 1 . The number of genetic clusters (K) present in the population was modelled using the programme STRUCTURE v.2.3.1 (Pritchard et al., 2000 ; Falush et al., 2003 ). The programme relies on a Bayesian clustering algorithm which we used to analyse the data with default settings and a burn-in period of 100 000, followed by 400 000 replicates with no prior population information. Values of K ranged from one to 10, with each K value replicated five times to assess the most likely number of inferred populations. The most likely K was assessed by calculating the mean likelihood, L(K), and implementing the ΔK method (Evanno et al., 2005 ) using STRUCTURE HARVESTER (Earl & vonHoldt, 2012 ) in Python 3. The web server CLUMPAK was used to summarize and visualize the STRUCTURE results (Kopelman et al. , 2015). We then mapped the individuals as before according to their cluster assignment using the folium library (Filipe et al 2020 ) in Python 3 (Van Rossum & Drake, 2009), and visualised using OpenStreetMap ( https://www.openstreetmap.org/ ) CC BY 3.0. Genetic differentiation was further evaluated with the software FSTAT version 2.9.3 (Goudet 1995 ) for the sampling locations listed in Fig. 1 . Pairwise F ST values were generated under 10,000 permutations and a Bonferroni correction for multiple tests was applied. The pairwise F ST values between them was considered as an index of isolation and a Mantel test was performed on the sampling localities to determine whether there was an overall correlation between geographic distance and genetic divergence (Smouse et al. , 1986). Using symmetric distance matrices, the Pearson correlation coefficient was calculated between the upper triangular elements of the two symmetric matrices using 10,000 permutations. In each permutation, the entries of one matrix were randomly shuffled, and the Pearson correlation coefficient was recalculated. The p-value was estimated as the proportion of permutations where the recalculated correlation coefficient was greater than or equal to the observed coefficient. Calculations were carried out using Python, with packages NumPy (Van Der Walt et al. , 2011), SciPy (Virtanen et al. , 2020), and Matplotlib (Hunter, 2007 ). Spatial autocorrelation analyses using GenAlEx 6.51b2 with 999 permutations was used to compare the pairwise relationship between genetic and spatial distance between pairs of individuals, and the Pearson correlation was calculated as before. Results Descriptive population statistics are provided in Table 1. The number of individuals successfully genotyped at each locus ranged from 184 to 206 and averaged 202. The number of alleles averaged 3.5. Levels of expected heterozygosity ranged from 0.449 at Mvi1341 to 0.698 at Mar-64 and averaged 0.567 across all loci. Levels of observed heterozygosity ranged from 0.325 at Mvis075 to 0.634 at Mar-64 and averaged 0.503 across all loci. F is (inbreeding coefficient) values ranged from -0.024 at Mvi1341 to 0.346 at Mvis075 and averaged 0.113 across all loci, with five of the 12 loci exhibiting significant deviation from Hardy-Weinberg equilibrium (P = 0.05) and four loci remained significant after Bonferroni correction (P = 0.004) (Table 1). Linkage disequilibrium was detected in 27 of the 66 possible pairwise comparisons at the 5% significance level, and three remained positive following Bonferroni correction (P = 0.0007). Table 1: Descriptive statistics for pine martens in Scotland across 12 microsatellite loci Mel1 Ma2 Mvi1341 Gg7 Mar-21 Mar-53 Mel105 MER041 Mar-08 Mvis075 Mar-64 Ma08 Average N 205 206 206 206 206 206 184 206 206 206 205 184 202.2 N a 3 4 4 4 4 3 4 3 4 2 4 3 3.5 H o 0.459 0.544 0.461 0.549 0.471 0.417 0.533 0.529 0.558 0.325 0.634 0.554 0.503 H e 0.508 0.584 0.449 0.674 0.497 0.459 0.630 0.631 0.607 0.496 0.698 0.568 0.567 F is 0.1 0.071 -0.024 0.189 0.056 0.093 0.157 0.164 0.083 0.346 0.093 0.027 0.113 Abbreviations are as follows: N = number of individuals genotyped at each locus, N a = number of alleles per locus; H e = expected heterozygosity; H o = observed heterozygosity; F is = inbreeding coefficient, with values in bold indicating significant deviation from Hardy–Weinberg equilibrium at P = 0.05; and bold values in italics indicating non significance after Bonferroni correction (P = 0.004). The number of alleles, expected and observed levels of heterozygosity were also calculated for the six general sampling zones outlined in Figure 1. The highest number of alleles was found in the Highlands (3.3) and the lowest number of alleles was found in Dumfries and Galloway (2.3). H o ranged from 0.545 in the Borders to 0.422 in the Cairngorms, and H e ranged from 0.546 in the Highlands to 0.423 in the Cairngorms (Table 2). Table 2: Average number of alleles, observed and expected levels of heterozygosity across sampling zones. Zone N a H o H e Aberdeen (n = 9) 2.9 0.509 0.492 Highlands (n = 92) 3.3 0.527 0.546 Cairngorms (n = 18) 2.8 0.442 0.423 Central (n = 33) 3.3 0.499 0.525 Borders (n = 11) 2.8 0.545 0.523 D&G (n = 35) 2.3 0.454 0.437 Abbreviations: D&G = Dumfries and Galloway The effective population size (N e ) calculated using the linkage disequilibrium method was estimated initially to be in between 22.7 (CI 18 – 28.3) and 23.7 (CI 19 – 29) using PCrit values of 0.05 and 0.02 which excludes rare alleles from the analysis, which may otherwise further decrease the estimates for effective population size. This analysis was repeated by dividing the data into the later defined STRUCTURE clusters (K = 3), and the results averaged an N e of 28.5 and CL values ranged from 21 – 42.2, providing slightly higher estimates than before, and suggesting that genetic structure had some impact on these estimates. The N e as defined by the sibship assignment method facilitated the inclusion of different mating scenarios and the following results were obtained for the following scenarios: both sexes being (a) polygamous (N e = 83 (CL 61 – 116)), (b) monogamous (N e = 137 (CL 107 – 176) , as well as (c) male-only polygamy and female monogamy (N e = 97 (CL 74 -128). All three of the models, IAM, TPM and SMM, and associated tests for significance, indicated the presence of a genetic bottleneck in the population (Table 3). Table 3: The probability values for the presence of a heterozygosity excess, assuming mutation-drift equilibrium as the null hypothesis, using the Infinite Allele (IAM), Two-phase (TPM) and Stepwise Mutation (SMM) models. Sign Test Standardised Difference Test Wilcoxon Test Mode Shift IAM TPM SMM IAM TPM SMM IAM TPM SMM 0.002 0.008 0.010 0.000 0.001 0.021 0.000 0.001 0.005 Yes The average M-ratio value was under the threshold < 0.7 which indicates that the population has experienced a reduction in population size (average 0.42; range 0.21 – 1). In this case, ten of the 12 loci used in this study had an M-ratio < 0.7. The PCoA (Figure 2), showed some evidence of genetic structure, with individuals from Dumfries and Galloway and the Cairngorms showing some evidence of genetic differentiation, while the majority of the remaining individuals clustered together. A total of 198 individuals were analysed for population-based assignment. The plot of the mean likelihood, L(K), established from combining each replicate per K value and associated standard deviation from STRUCTURE HARVESTER showed a peak in the dataset at K = 5 (Fig. 3a). The delta K showed that K = 3 was the most likely number of genetic clusters within the population, and a small peak also occurred at K = 5 (Fig. 3A) (Evanno et al., 2005). As Figure 3 (a & b) did not conclusively determine the true number of clusters (K), K values from 2 to 5 were examined in Figure 4. The analysis revealed that samples from Dumfries and Galloway formed the first distinct cluster (blue) separating from the dataset at K = 2, with notable admixture observed as animals from the Highlands, Central, and Borders regions also aligned with this blue cluster. The Cairngorms were predominantly represented in the second cluster (orange), which also included a significant proportion of animals from Aberdeenshire, the Highlands, Central, and Borders. At K = 3, most of the Dumfries and Galloway samples distinctly segregated into a third cluster (purple), with a few animals from the Highlands (n = 3), Central (n = 1), and Borders (n = 2) showing affinity for this cluster. Interestingly, two animals from Dumfries and Galloway were identified with the blue cluster, primarily associated with the Highlands. By K = 4, the Cairngorms distinctly emerged as a separate cluster, yet shared similarities with groups from Aberdeen, the Highlands, Central, and Borders. The blue cluster continued to be dominant in the Highlands, with a substantial number of animals from Aberdeen, Central, and Borders, including two from Dumfries and Galloway, also falling into this cluster. Additionally, at K = 4, a new cluster (green) appeared, mainly within the Highlands and among a few individuals in other regions, excluding the Cairngorms. This cluster was less defined, except for several Highland animals with strong assignments. Finally, at K = 5, another cluster (purple) was most distinct in the Highlands, with a few animals in Central and Borders also exhibiting a connection to this cluster. The Cairngorms, Central (partial) and Dumfries and Galloway formed the most distinct clusters at K = 5. Animals from Aberdeen, Highlands, Borders and parts of Central all contained high levels of admixture. F is was calculated for clusters defined at K = 3 and only one cluster had a moderately F is at 0.068 and remained significant following Bonferroni correction (P = 0.0014). As the presence of genetic structure was evident in the Cairngorms and Highlands, K = 3 was mapped to help understand if geographic barriers influenced the structure present (Figure 5). Significant pairwise F st values varied, with the highest being 0.236 between the Highlands and the Cairngorms, and the lowest significant value at 0.044 between Dumfries and Galloway and Central. Conversely, low and nonsignificant F st values were observed between Aberdeen and Central, between Borders and Dumfries and Galloway, and between Dumfries and Galloway and the Scottish Borders, indicating minimal genetic differentiation in these pairs (Table 4) Table 4: Pairwise F st values across sampling sites, with significant differences bolded for emphasis. P-values have been adjusted using a Bonferroni correction, setting the significance cut-off at P = 0.003 to reduce the risk of false positive results in the dataset. Aberdeen Highlands Cairngorms Central Borders Highlands 0.187 Cairngorms 0.192 0.236 Central -0.001 0.169 0.134 Borders 0.050 0.186 0.179 0.072 D&G 0.043 0.219 0.179 0.044 0.051 The Mantel test comparing F st and geographic distances between sampling localities revealed a weak, negative correlation (r = -0.071, P = 0.603), indicating no significant link between genetic differentiation and geographic distances among sampled areas. Conversely, a separate Mantel test on genetic and geographic distances between individuals showed a weak, positive correlation (r = 0.0297, p = 0.00003), marking a significant, but very small, association between genetic and geographic distances across individuals. Discussion In this study we aimed to assess the genetic diversity of the contemporary Scottish pine marten population using microsatellites. Our goal was to gain insights into the impact past declines have on the current population, which is steadily expanding and recolonising its historical range. This is crucial for informing future conservation management and supporting the ongoing recovery of the species in Britain. In this study, genetic diversity was assessed using a panel of 12 microsatellite markers. These markers, carefully selected and optimised over time for their variability and ability to amplify pine marten DNA from poor-quality samples, such as hair from non-invasive genetic studies (e.g. Mullins et al., 2010; Croose et al., 2019; Twining et al., 2022), are arguably biased towards this variability. Although microsatellite markers are a cost-effective and reliable method for assessing genetic diversity in various species, comparisons with SNP data from restriction-site-associated DNA sequencing (RADseq) suggest that SNPs may provide deeper historical population insights. This is due to their slower mutation rate compared to microsatellite regions, making them particularly useful in small, isolated populations with low diversity. Indeed, many newer studies, some of which are discussed below, take a genomic approach to provide genetic diversity assessments of recovering species such as the wolf ( Canis lupus ) (vonHoldt et al., 2023). While the cost implications and overall benefits of transitioning from a well-developed microsatellite panel should also be considered (Lemopoulos et al., 2019; Hauser et al., 2021), a genomic approach could be considered for future monitoring of the pine marten. The sampling method employed in this study was ad hoc, and some samples, now 10 years old, provide a genetic snapshot that is likely out-of-date, given the rapid expansion of the species. However, this study could serve as a valuable foundation for future studies, which should aim for more systematic sampling across the species' range. In terms of nuclear genetic diversity, the number of alleles across the selected microsatellite loci in the Scottish population averaged 3.5, with H o and H e averaging 0.503 and 0.567. These figures are slightly lower than the values reported for the pine marten in Ireland, where the number of alleles averaged 4.3 and observed and expected levels of heterozygosity averaged 0.547 and 0.549, respectively (O’Reilly et al., 2021). Only the expected levels of heterozygosity are slightly higher in the Scottish population. O’Reilly et al., (2021) used 11 out of 12 of the same microsatellite markers, making comparisons reliable across these two studies. Similar levels of genetic diversity were noted in pine marten populations recorded in Spain (Ruiz-González et al., 2014), Denmark (Pertoldi et al., 2008), and France (Mergey et al., 2012). In North America averages of H o and H e were similar for M. americana (H o = 0.57, H e = 0.58) and M. caurina (H o = 0.57, H e = 0.56) (Lucid et al., 2020), suggesting that the results obtained in this study are similar across the range of this species and similar species’ ranges. Notably, genetic diversity in the Cairngorms (H o = 0.44, H e = 0.42) and Dumfries and Galloway (H o = 0.45, H e = 0.44) was lower than average. This was anticipated in Dumfries and Galloway, where 12 pine martens were translocated from the Highlands in the 1980s, likely leading to genetic isolation. Genetic differentiation tests confirmed this region's uniqueness, as indicated by its early separation in the STRUCTURE analysis and differentiation in the PCOA. Significant F st values further underscored this isolation, particularly when compared with other sampled zones. However, these values decreased closer to the Scottish Borders and Central Scotland, indicating some gene flow. Surprisingly, the highest F st values were observed with the Highlands, the source of the translocation, suggesting genetic drift has made this population genetically distinct. STRUCTURE plots revealed admixture in some individuals at K = 3, indicating genetic mixing between animals from Dumfries and Galloway, Highlands, Central and Borders, but it remains unclear whether this reflects past or more recent gene flow. The genetic isolation observed in the Cairngorms, also supported by differentiation tests, may be attributed to geographical barriers, such as high terrain, the River Spey, and several lochs, alongside the presence of the A9 roadway, causing further isolation. Indeed, the rugged terrain of the Scottish Highlands, characterised by mountainous landscapes, numerous lochs (lakes), deep glens (valleys), and expansive moorlands, might have contributed to the fragmentation of species across Scotland. This fragmentation could have led to the formation of distinct genetic clusters, which are now mixing as individuals recolonise Scotland, producing a genetically mixed population in many areas. Our results, analysed at individual level, suggest evidence of very weak isolation by distance within the study population, with geographically closer individuals tending to be more genetically similar. There was no association between genetic differentiation and geography when we analysed the data according to sampling zone, and it is likely that most of the genetic differentiation is influenced by other factors, which could include reintroductions, opportunistic ad hoc sampling selection, migration, genetic drift, or non-random mating patterns. Consequently, it is unclear how this genetic fragmentation of the population occurred. Beyond the known translocation to Dumfries and Galloway, there have been unofficial releases in the Scottish Borders, and possibly more that have gone undocumented, as evidenced by reports of unofficial introductions on the Isle of Mull (Solow et al., 2013). Such undocumented actions could have contributed to the observed levels of genetic mixing in this study. It is also likely that the genetic bottlenecks and inbreeding detected in our study have been influenced by unofficial translocations involving small numbers of animals. This could potentially create multiple, relatively recent founder effects in the population, thus complicating the use of conservation genetics to infer adaptive management strategies. Five out of the 12 loci exhibited significantly positive F is values. While positive F is values can indicate inbreeding, they are also associated with genetic admixture, which is clear in this case. Analysing populations with underlying genetic structure for genetic diversity can artificially increase the level of homozygotes at the expense of heterozygotes, a phenomenon perceived as inbreeding. This is known as the Wahlund effect, which occurs when a population, composed of two or more subpopulations, is analysed as if it were a single, homogeneous population (Waples, 2015). However, in this study, defining populations for analysis was not straightforward, as most sampled localities exhibited genetic structure or sub structure that could not be easily separated for analysis. Indeed, it is rare in population genetics that 'ideal' populations are ever sampled from a statistical viewpoint. Therefore, the results should be interpreted with this consideration in mind (De Meeûs, 2018). A study of European rabbit populations across 17 sites in the East Anglian region of Britain, following a population crash due to a myxomatosis outbreak, found that the populations became genetically distinct with low effective population sizes. It suggested that this genetic divergence resulted from the myxomatosis-induced crash, combined with reproductive and social characteristics that influenced the genetics rather than from past historical events (Surridge et al., 1999). It is possible that a similar scenario exists in this study, where the pine marten population may retain genetic signatures of past population declines. There was evidence of a genetic bottleneck across all tests implemented in this study. This was also confirmed by the mode-shift test. It is important to note that the Wahlund effect can confound these patterns, making it challenging to distinguish between the loss of alleles due to a bottleneck and the artificial reduction in heterozygosity due to population substructure. However, the mode-shift test is designed to detect shifts in the distribution of allele frequencies indicative of a bottleneck, making it less influenced by the presence of the Wahlund effect, but the results should be viewed with this implication. However, some evidence of a genetic bottleneck was also found in the Irish pine marten population, which was not impacted by genetic structure (O’Reilly et al., 2021). The mode shift test also indicated a shifted mode, which did not conform to the L-shaped distribution expected to occur in a non-bottlenecked population. The observed shift is associated with the loss of low-frequency alleles due to a decline in the Scottish pine marten population size, signifying a genetic bottleneck. When a population undergoes a bottleneck, there is a distinct mode-shift distortion in allele frequencies. Specifically, alleles with a low frequency (less than 0.1) become rarer compared to those in an intermediate range (e.g. 0.1–0.2). An L-shaped distribution suggests no recent bottlenecks for several generations; but a shifted mode, as seen in this study, signifies a recent genetic bottleneck (Luikart et al., 1998; Piry et al., 1999). Similar genetic bottlenecks were not detected, when previously tested for, in France (Mergey et al., 2012) or Sardinia (Coli et al., 2011), but Ruiz-Gonzalez et al., (2015) did find significant support for historical reductions in effective population sizes in the north of Spain, which was attributed to habitat fragmentation and the presence of a competing mustelid, Martes foina , but there were also unknown factors that may have contributed to a reduction of gene flow. For example, it was proposed that the pine marten was expanding its range in parts of northern Spain, and a lag time, akin to what this study proposes, may have hindered landscape features from being reflected in the species' genetic structure. The detection of a potential bottleneck signature in the present study could reflect the pine marten’s past population retraction, as these signatures can remain for one hundred years or more, even in variable loci like microsatellites. Regardless of the direct cause of the result in this study, be it a past bottleneck or the impact of genetic structure, increasing contemporary gene flow within Scotland would benefit the diversity of the species. The effective population size estimates from this study ranged from depressed (less than 50), as determined by the linkage disequilibrium method, to modest (less than 200), as determined by the sibship method, all falling below the putative 500-threshold required for long-term viability (Frankham et al., 2014). The variation in results can be attributed to the different underlying assumptions of the methods used to generate these estimates. The linkage disequilibrium method, which assumes observed linkage disequilibrium originates solely from genetic drift in a unified population, may underestimate the effective population size. This underestimation can occur due to the Wahlund effect, which inflates linkage disequilibrium and could misinterpret the extent of population size or drift in structured populations (Waples and Do, 2010). This scenario is very likely in our study, given the underlying genetic structure across most of Scotland. Conversely, the higher estimate derived from the sibship method offers a more reliable means for deriving effective population size estimates in cases like ours, where samples are not collected systematically and may include related individuals across different generations. Less impacted by the Wahlund effect compared to the linkage disequilibrium method, the sibship method focuses on identifying full and half-sibling groups within a sample based on genetic similarity among individuals. This approach avoids reliance on population-level allele frequencies or linkage disequilibrium patterns, making it more reliable for our study. Waples (2021) found that the linkage disequilibrium method outperformed the sibship method in terms of precision, but Gilbert & Whitlock, (2015) also warned that the accuracy of any method is dependent on the demography of the species. What we can say in this case is that the effective population was found to be modest using two independent methods, something which Waples (2021) says improves the overall precision of the estimates. Estimates for effective population size for Martes americana and M. caurina , also derived using the same linkage disequilibrium method, were also generally modest (in the hundreds) across a 53,474 km 2 area, encompassing portions of British Columbia, Idaho, Montana, and Washington (Lucid et al., 2020), and suggested that the long-term protection of the species in the region depended on corridor conservation efforts. Similar estimates were derived for the recovering pine marten in Ireland (O’Reilly et al., 2021). In Finland, the wolverine ( Gulo gulo ) is showing recovery, especially in the north and east, thanks to translocations in the late 20th century (Lansink et al., 2020). Two primary clusters displayed limited gene flow, with heterozygosity levels of H e = 0.49 and H e = 0.57, and average alleles of 3.8 and 4.0 – but the effective population size was under 50, indicating previous genetic bottlenecks and future viability concerns (Frankham et al., 2014). It appears that estimates of low to modest effective population size below 500 are not uncommon in mustelid species, particularly those that have had past and current population stresses. For species whose populations have been anthropogenically reduced, such as the grey wolf, and are now expanding, the effective population size can be significantly smaller than the census population count. In 2021, the northern Rocky Mountains had a census size estimated at 3,354, and the western Great Lakes at 4,526, but when these values were converted to an effective population size, they ranged between 201 and 335 wolves for the northern Rocky Mountains, and between 272 and 453 for the western Great Lakes. Expressing the census population as a multiple of the effective population size reveals that the census population of grey wolves is estimated to be between 10 and 18 times larger than its effective population size. Given the strong skew in the effective-to-census size ratio in grey wolves, conservation practitioners aim to maintain larger wolf populations to ensure long-term adaptation and survival (vonHoldt et al., in 2023). However, achieving the required number is challenging, considering that the species' dispersal capabilities and success must be considered. To maintain and increase effective population size, vonHoldt et al., (2023) recommend that dispersers be granted protection. Indeed, there are many parallels in the pine marten population in this study, as this species was historically heavily persecuted and is now expanding. However, migrants too face barriers to dispersal, particularly roads. Based on the census estimate of 3,700 (Mathews & Harrower, 2020) and the effective population sizes derived from sibships, ranging from 83 to 137, the ratio of the census population to the effective population size in pine martens is estimated to be between 27 and 44 times larger. The strong skew in the effective-to-census size ratio in pine martens also implies that larger populations are necessary to ensure long-term adaptation and survival, as is the case with wolves. The higher multiplier in pine martens compared to grey wolves may stem from challenges encountered in accurately estimating effective population sizes as previously discussed, and could also reflect variations in social organisation, complicating cross-study comparisons. vonHoldt et al., (2023) recommended that studies should be carried out periodically to reassess the situation, which could also be applied in this case. Given the uncertainty raised in this study related to the genetic recovery of the pine marten, which appears to be lagging behind its national demographic recovery, it is timely to suggest that long-term genetic population monitoring be adopted for the species throughout Britain including the re-establishing populations in England and Wales. Notably, such monitoring is already being planned for the restored population in Wales by Vincent Wildlife Trust (VWT) (J. MacPherson pers. comm.). In our experience, methods involving the collection of hair samples using tubes or feeders have consistently yielded good-quality DNA suitable for genotyping, as demonstrated in this study. Source populations in Scotland should undergo similar checks every decade. The feasibility of implementing a nationwide and standardised approach to long-term population and genetic monitoring should be evaluated and discussed among stakeholders. Methods utilised by Sheehy et al. (2018) in Scotland and O’Mahony et al. (2017a) in Ireland, along with density modelling techniques combined with genotyping as outlined in Twining et al. (2022), offer promising frameworks for such nationwide initiatives. By doing so, we can ensure the health and sustainability of these populations, using this study as a foundational reference for future efforts, and plan adaptive interventions accordingly. When effectively carried out, these efforts could shape future policy, legislation, and guidelines concerning the restoration of wildlife species. This precautionary approach seems wise considering the impacts of global heating and climate breakdown upon pine martens and the habitats upon which they depend. Given the parallels within Britain and Ireland (O’Reilly et al., 2021), there is an opportunity to collaborate on understanding the recovery of the pine marten. This collaboration could inform and establish best practices for other conservation recovery projects, especially as the field of genetic reinforcement is still in its early stages. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Relevant funding sources related to sample collection and molecular analysis were previously declared in previously published cited material in the present study. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions COR, ES, JMacP and DOM conceived the study. COR carried out the molecular laboratory work. COR and DOM analysed the data and wrote the initial draft of the paper, and all authors contributed to subsequent drafts. Data Availability The dataset generated and analysed during the current study is available from the corresponding author on reasonable request. Acknowledgements The authors gratefully acknowledge Dr. Nicholas Aebischer for his valuable comments pertaining to the discussion. References Birks JDS (2020) Pine Martens. 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Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Conservation Genetics → Version 1 posted Editorial decision: Revision requested 29 Oct, 2024 Reviews received at journal 12 Jul, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 21 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviewers invited by journal 12 Mar, 2024 Submission checks completed at journal 02 Mar, 2024 Editor assigned by journal 02 Mar, 2024 First submitted to journal 28 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3997852","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275936325,"identity":"1f3b062b-630e-4125-a2ae-3b75dbac03c5","order_by":0,"name":"Catherine O’Reilly","email":"","orcid":"","institution":"South East Technological University (SETU)","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"O’Reilly","suffix":""},{"id":275936326,"identity":"ffc745d6-96ec-4360-b2c6-57219cf24a1c","order_by":1,"name":"Emma Sheehy","email":"","orcid":"","institution":"South East Technological University (SETU)","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Sheehy","suffix":""},{"id":275936327,"identity":"5a0e3e07-3865-4a20-ad11-a1b8aac5888d","order_by":2,"name":"Jenny MacPherson","email":"","orcid":"","institution":"Vincent Wildlife Trust","correspondingAuthor":false,"prefix":"","firstName":"Jenny","middleName":"","lastName":"MacPherson","suffix":""},{"id":275936328,"identity":"cca90b6f-5062-45ce-b4da-11418f5b4f85","order_by":3,"name":"Johnny Birks","email":"","orcid":"","institution":"Swift Ecology Ltd","correspondingAuthor":false,"prefix":"","firstName":"Johnny","middleName":"","lastName":"Birks","suffix":""},{"id":275936329,"identity":"2be36e75-9096-4180-a8e6-ebc6fbb6d417","order_by":4,"name":"John Martin","email":"","orcid":"","institution":"Myotismart Ltd, Horncop Bungalow","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Martin","suffix":""},{"id":275936330,"identity":"c8685f42-de9f-4598-80f6-693885eebc4c","order_by":5,"name":"Elizabeth Croose","email":"","orcid":"","institution":"Vincent Wildlife Trust","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Croose","suffix":""},{"id":275936331,"identity":"e63dc6e6-8a20-4077-b3e4-78a0e4c152cf","order_by":6,"name":"Kathy Fletcher","email":"","orcid":"","institution":"Game \u0026 Wildlife Conservation Trust","correspondingAuthor":false,"prefix":"","firstName":"Kathy","middleName":"","lastName":"Fletcher","suffix":""},{"id":275936332,"identity":"26ec6e29-49ae-4190-a8b9-bb9e1853e9ce","order_by":7,"name":"Xavier Lambin","email":"","orcid":"","institution":"University of Aberdeen","correspondingAuthor":false,"prefix":"","firstName":"Xavier","middleName":"","lastName":"Lambin","suffix":""},{"id":275936333,"identity":"ab84fff0-a300-48bb-a959-fa79913f201e","order_by":8,"name":"Thomas Curran","email":"","orcid":"","institution":"South East Technological University (SETU)","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Curran","suffix":""},{"id":275936334,"identity":"d47df270-942f-4839-90eb-926009ee2744","order_by":9,"name":"Rebecca Synnott","email":"","orcid":"","institution":"South East Technological University (SETU)","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Synnott","suffix":""},{"id":275936335,"identity":"ef21bd21-7433-4fc3-a999-598fa68b44b0","order_by":10,"name":"Denise O’Meara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDCCA2AEAxVwQaK1nGFg4CFGCwIwtkG04AV8x3sPHvxRwSCn29787MPPeXVy9hIJjAc+4NEieeZcwmGeMwzGZmeOGc/s3XbYmEcigeHgDDxaDG7kGBwGuidx240EYwbebQcSe4BaDuNzncH9NwYHf/4Darn//DPj3zl19WAtf/DawmNwgLcBZAuPMTNvA3MCyGGH8Xlf8gzQYTzHJIB+ySlmljl22LDnzMOGgz14tPAdP2P88UeNjZzZ8eObGd/U1Mmztycf/vADnzUQIIHMYWwgrGEUjIJRMApGAV4AAIObVFx3jw2oAAAAAElFTkSuQmCC","orcid":"","institution":"South East Technological University (SETU)","correspondingAuthor":true,"prefix":"","firstName":"Denise","middleName":"","lastName":"O’Meara","suffix":""}],"badges":[],"createdAt":"2024-02-28 21:50:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3997852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3997852/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10592-024-01660-4","type":"published","date":"2024-11-23T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52036879,"identity":"864fca42-3911-4744-8ec6-f1e713f8ed16","added_by":"auto","created_at":"2024-03-05 17:14:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120888,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of 198 grid references samples used in this study, a geographic area encompassing 31,706.31 km2. The sampling zones, as circled, broadly correspond to the following locations: (1) Aberdeen and North East (N = 9); (2) Highlands (N = 92); (3) Cairngorms (N = 18); (4) Central incorporating Clackmannanshire and Angus (N = 33); (5) Scottish Borders (N = 11); (6) Dumfries and Galloway (N = 35). The location of Scotland is shown in context of Britain, Ireland and northern France.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3997852/v1/9c9974ad0cbc385803dcc77d.jpeg"},{"id":52036262,"identity":"bf6f58fb-ac90-458e-8da6-7a2254d34765","added_by":"auto","created_at":"2024-03-05 17:06:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48065,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinate analysis of individual pine martens colour coded by sampling zone. Axis 1 described 29.2% and Axis 2 describes 23.74 %, cumulating to an explanation of 52.9% of the genetic variation within the data.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3997852/v1/13b68c08b3d42696f4167139.jpeg"},{"id":52036261,"identity":"dabfbcc6-ee78-4e9d-8a1d-ab746cd6cf07","added_by":"auto","created_at":"2024-03-05 17:06:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68611,"visible":true,"origin":"","legend":"\u003cp\u003e(a) A plot of mean likelihood L (K) and standard deviation per K value of 197 individual pine martens genotyped at 12 microsatellite loci. (b) Plot of Evanno’s ΔK method (Evanno \u003cem\u003eet al.,\u003c/em\u003e 2005).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3997852/v1/81a87fa8f437e71fcb4ded4c.jpeg"},{"id":52036260,"identity":"3095cab2-f671-4bc3-9208-6f8de581c349","added_by":"auto","created_at":"2024-03-05 17:06:57","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104941,"visible":true,"origin":"","legend":"\u003cp\u003eMembership of individual pine martens to K=2, 3, 4 and 5 as inferred by STRUCTURE analysis. Each pine marten is represented by a single vertical bar.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3997852/v1/2fbe95f75f279e8f53bdb1b1.jpeg"},{"id":52036264,"identity":"0421c253-1db6-4184-bb98-723e9f5de8ba","added_by":"auto","created_at":"2024-03-05 17:06:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1410127,"visible":true,"origin":"","legend":"\u003cp\u003eK = 3 mapped across the north of Scotland.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3997852/v1/fcb9f8636eda8befbc087f00.png"},{"id":69834980,"identity":"d36e86a8-424e-4cc5-ab65-0e252d35ac7e","added_by":"auto","created_at":"2024-11-25 16:11:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2219084,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3997852/v1/92ea068b-bc1f-418d-bafc-1446d6fd01e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Lag in a Demographically Recovering Carnivore: The Case of the British Pine Marten (Martes martes)","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePine martens (\u003cem\u003eMartes martes\u003c/em\u003e), a medium sized mustelid, on the islands of mainland Britain and Ireland have endured what Birks (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) described as a \"horrendous history\" since their heyday in the British Mesolithic and Irish Neolithic periods (Montgomery et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Since then, their populations declined drastically, resulting in widescale regional extinctions in both countries due to human impacts. Starting in the Neolithic in Britain, the near-complete clearance of woodland - the optimal pine marten habitat - to just five percent cover in the early 1900s (Forestry Commission, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and its replacement with open agricultural land, was the main impact. This was exacerbated by additional factors such as fur trapping, killing martens as perceived vermin, and gamekeeping and hunting (Tapper, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Croose et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Birks \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; O\u0026rsquo;Reilly et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, in certain areas of Britain and Ireland, pockets of pine martens managed to survive (Birks, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; O'Reilly \u003cem\u003eet al.\u003c/em\u003e, 2021). In Ireland, since reaching its nadir in the early 20th century, the pine marten population has significantly expanded, aided by legal protection, and increased tree cover (O\u0026rsquo;Mahony et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e\u0026amp;b; Lawton et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, in Britain, the species reached its lowest point in the early 20th century. It is thought to have primarily contracted to the Northwest Highlands of Scotland, an area characterised by rocky terrain, fragmented woodlands, and minimal human interference. Additionally, smaller, isolated populations are assumed to have persisted in upland areas of England and Wales (Langley \u0026amp; Yalden, 1977). Since the 20th century, the Scottish population has experienced significant recovery due to reduced trapping, thanks to legal protection, increased forest cover, and translocations (Shaw \u0026amp; Livingstone \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Croose et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwelve pine martens (comprising six males and six females) were relocated from western Inverness-shire, to Galloway Forest Park in southwest Scotland between 1980 and 1981 (Shaw \u0026amp; Livingstone, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). More recent relocations have also occurred, including 14 animals, rescued as orphans in the Highlands, later unofficially reintroduced into the Scottish Borders following rehabilitation (Croose et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Reliable sources indicate an ongoing programme of unofficial releases in southern Scotland, involving young pine martens rescued as orphans from various parts of Scotland (J. Birks \u0026amp; J. Martin, Pers. obs.).\u003c/p\u003e \u003cp\u003eThe most recent population estimate for pine martens in Britain is approximately 3,700, with a 95% confidence interval ranging from 1,600 to 8,900 (Mathews \u0026amp; Harrower, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), an estimate closely aligned with the one provided by Harris et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), though derived through a different calculation method. Given the demographic expansion observed in Scotland, Mathews \u0026amp; Harrower (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggested that the actual number might trend towards the higher end of their estimate. Consequently, certain regions within the Scottish Highlands were assessed as stable enough to permit the sustainable removal and translocation of animals for population reinforcement and restoration projects in Wales and England (MacPherson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). From 2015 to 2017, 51 pine martens were taken to Wales (McNicol et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, between 2019 and 2021, a reintroduction to the Forest of Dean in Gloucestershire received pine martens taken from Scotland, starting with a population of 35 individuals (E. Croose \u0026amp; J. MacPherson, Pers. obs.).\u003c/p\u003e \u003cp\u003eThere is growing evidence to suggest that the restoration of native vertebrates can contribute to landscape-scale biological control of established invasive vertebrates (Twining et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Restoration efforts are often driven by community desires to restore native species, curb biodiversity loss, and reap the ecosystem benefits of pine martens in potentially controlling invasive species like the North American grey squirrel (\u003cem\u003eSciurus carolinensis\u003c/em\u003e) as culling efforts are not proving successful in the long-term (Synnott et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, pine martens, at healthy population densities, may aid the recovery of other native species like the red squirrel (\u003cem\u003eSciurus vulgaris\u003c/em\u003e) (Sheehy \u0026amp; Lawton, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sheehy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Twining et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The mechanisms behind these interactions are not yet fully understood, but molecular tools have confirmed predation of grey squirrels by pine martens (O\u0026rsquo;Meara et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sheehy et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Britain and Ireland, where large carnivores have become extinct, restoration efforts for remaining species are hindered by the lack of natural inward migration from the continent, essential for maintaining genetic diversity. O'Reilly \u003cem\u003eet al.\u003c/em\u003e, (2021) reported moderate genetic diversity within the recovering Irish pine marten population (average He and H\u003csub\u003e0\u003c/sub\u003e 0.55 and 4.3 alleles), alongside evidence of genetic bottlenecks and a modest effective population size. Similarly, despite the demographic resurgence of Britain's otter (\u003cem\u003eLutra lutra\u003c/em\u003e) population, genetic studies, including Thomas et al., (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), reveal a potential genetic lag, a term used to broadly explain how a population\u0026rsquo;s genetic recovery may not keep pace with a demographic expansion. Analysis of 90 otter samples from 2014 showed average expected heterozygosity (H\u003csub\u003ee\u003c/sub\u003e) between 0.45 and 0.67, observed heterozygosity (H\u003csub\u003eo\u003c/sub\u003e) between 0.37 and 0.55, and allele counts between 3 and 4.2. The 1994 data, based on only 28 samples, exhibited H\u003csub\u003ee\u003c/sub\u003e values from 0.48 to 0.66, H\u003csub\u003eo\u003c/sub\u003e from 0.44 to 0.7, and allele counts from 1.5 to 3.2, suggesting that demographic recovery does not necessarily lead to genetic recovery. In the development of conservation strategies for recovering species such as the pine marten and otter, it is important to take genetic factors into account, especially when concerns about genetic diversity remain despite population growth.\u003c/p\u003e \u003cp\u003eIn contrast to the genetic diversity found in recovering native species, many invasive species appear to be genetically thriving, most likely due to divergent and mixed origins of the species, making some of the invasive populations more diverse than their native counterparts. The North American mink (\u003cem\u003eNeovison vison\u003c/em\u003e), imported into Europe originally for fur farming, has extensive levels of genetic diversity in Europe, with 31 different mtDNA haplotypes found within Poland alone (Zalewski \u003cem\u003eet al.\u003c/em\u003e, 2011). A genetic study in Scotland showed differing levels of genetic diversity according to population and location. The largest sample containing 273 individuals had an average H\u003csub\u003ee\u003c/sub\u003e of 0.62 and H\u003csub\u003eo\u003c/sub\u003e of 0.61, and an average of 7.2 alleles, but these numbers declined in other sampling areas with smaller sample sizes e.g. one site with 30 animals had a H\u003csub\u003ee\u003c/sub\u003e of 0.4 and H\u003csub\u003eo\u003c/sub\u003e of 0.39 and 3.2 alleles (Fraser \u003cem\u003eet al.\u003c/em\u003e, 2013). The invasive North American grey squirrel in North Wales also had high genetic diversity, with H\u003csub\u003ee\u003c/sub\u003e and H\u003csub\u003eo\u003c/sub\u003e averaging 0.56 and 0.35 respectively, and the number of alleles averaging 6.7 (Synnott et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite regular culling of the species in North Wales, over a 10 year period, the population was not found to have experienced any significant decreases in diversity, due to the inward migration of new animals with divergent genetic origins in North America, encompassing a total of six distinct mtDNA haplotypes within a relatively small geographic area. While there are many factors contributing to the success of invasive species, the examples provided illustrate how high levels of genetic diversity, resulting from divergent and mixed origins, can complicate efforts to manage or exterminate these species, a contrast to the challenges faced by some native species, which, in certain cases, suffer from reduced genetic viability.\u003c/p\u003e \u003cp\u003eDespite our general understanding that higher genetic diversity can lead to greater population resilience (Frankham et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), studies such as those of the otter in Britain and the pine marten in Ireland have shown that despite spatial and demographic recovery, genetics can sometimes lag behind this demographic recovery. While obvious population viability problems may not exist now, it is possible that issues could arise in the future due to reduced viability and evolutionary potential. Thomas et al., (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), noted that few studies exist which can provide empirical evidence of the time needed for genetic recovery of a species following demographic recovery, or indeed if other anthropogenic factors impede expected increases in genetic diversity, in parallel with demographic recovery (Hoban et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to provide genetic insights to enhance ongoing translocation efforts, maximizing their effectiveness, and to facilitate adaptive decision-making by supplying data crucial for protecting the genetic integrity and viability of the British pine marten. This approach is expected to align with and support sustainable, long-term conservation and management practices in accordance with the Pine Marten Recovery Programme (MacPherson \u0026amp; Wright, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eA total of 206 samples collected over a period spanning three years between 2014 and 2017 were used in this study and were generated as part of a number of studies and reports including Sheehy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e (n\u0026thinsp;=\u0026thinsp;94), collected between 2015 and 2016; the Game and Wildlife Conservation Trust (GWCT) (n\u0026thinsp;=\u0026thinsp;18), collected in 2015, The Vincent Wildlife Trust (n\u0026thinsp;=\u0026thinsp;59), collected between 2015 and 2016 and a study in Dumfries and Galloway (Croose et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (n\u0026thinsp;=\u0026thinsp;35), collected in 2014 and 2017. Most of the samples were hair samples collected via non-invasive hair-tube studies using the method described in Mullins et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) or squirrel feeders with glue patches (Sheehy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and a total of seven opportunistically collected roadkill samples from Scotland were also included which did not have defined grid references. The samples were not collected for the purpose of a population genetics study. However, they eventually offered an opportunity to be compiled for that purpose, but due to the nature of the sampling process, the distribution of samples is uneven, representing ad-hoc 'sampling zones' in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe distribution of 198 grid referenced samples used in this study is mapped in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Individual sample locations were mapped using the following programmes and libraries in Python 3 (Van Rossum \u0026amp; Drake, 2009): Rasterio (Gillies \u003cem\u003eet al.\u003c/em\u003e, 2019); Matplotlib (Hunter \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); and GeoPandas (Van den Bossche et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We used the 'Scotland\u0026rsquo;s Wildness \u0026ndash; Ruggedness' layer for visualisation to showcase the natural ruggedness of Scotland's landscape. This layer is made available by Scottish Natural Heritage (SNH) and is accessible under the OS Open Data licence. It relies on Ordnance Survey data \u0026copy; Crown copyright to accurately represent the rugged or physically challenging terrain. The layer's documentation can be found on the Spatialdata.Gov.Scot Metadata Portal, a resource managed by the Scottish Government and Astun Technology (2019), and is licensed under the Open Government Licence \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.nationalarchives.gov.uk/doc/open-government-licence\" target=\"_blank\"\u003ewww.nationalarchives.gov.uk/doc/open-government-licence\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.nationalarchives.gov.uk/doc/open-government-licence\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. To calculate the size of the sampling area, a bounding box was used, by determining the maximum and minimum X and Y values, creating a box around the data, and calculating the size of the area. To provide context of geographic positioning of Scotland in relation to Britain, Ireland and the north of France, a map was made with Natural Earth, a provider of free vector and raster map data \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.nationalarchives.gov.uk/doc/open-government-licence\" target=\"_blank\"\u003ewww.naturalearthdata.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.naturalearthdata.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Analysis\u003c/h2\u003e \u003cp\u003eDNA was extracted from samples and confirmed to species via qPCR using the methods outlined in O\u0026rsquo;Reilly et al., (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); Mullins et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)d Reilly et al., (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Samples were genotyped in triplicate at 12 microsatellite loci as per O\u0026rsquo;Reilly et al., (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Croose et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) using the following markers: 12 microsatellite markers: Mel1, Ma2, Gg7, Mar21, Mvis1341 Mar53, Mar08, Mel105, Ma08, Mar64, Mer041 and Mvis075 (Davis \u0026amp; Strobeck, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Carpenter et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Fleming et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Natali, 2010; O\u0026rsquo;Reilly et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These markers were chosen for their high amplifiability in low-quality DNA samples, such as hair, and their proven efficacy in identifying unique individuals. Additionally, they were selected for their highly polymorphic nature, making them particularly suitable for assessing genetic diversity. This selection ensures that the most variable markers available are utilised, thereby providing a comprehensive understanding of the population's genetic diversity, but this also means that the markers are specifically chosen for characteristics that may not be uniformly distributed across all potential genetic markers in the population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe number of alleles (A) and observed and expected heterozygosities (H\u003csub\u003eo\u003c/sub\u003e and H\u003csub\u003ee\u003c/sub\u003e) were calculated via GENALEX v.6.5b (Peakall \u0026amp; Smouse, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The inbreeding coefficient (F\u003csub\u003eis\u003c/sub\u003e) was calculated using FSTAT v.2.9.3 (Goudet, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The significance levels were determined by randomising the alleles within the population, using 10,000 permutations, and then comparing these with the observed data, an approach used to assess the presence of deviations from the Hardy\u0026ndash;Weinberg equilibrium. Tests for linkage disequilibrium were performed between pairs of loci using FSTAT v.2.9.3 (Goudet, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eCalculation of effective population size and detection of genetic bottlenecks\u003c/h2\u003e \u003cp\u003eThe programme NeEstimator v.2 (Do et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was used to calculate the effective population size (Ne) and associated 95% confidence intervals using the linkage disequilibrium method developed by Waples \u0026amp; Do (2008) at two levels of lowest allele frequency (0.02, 0.05). Effective population size was also calculated using the sibship assignment method using the programme COLONY v2.0.7.0 (Jones \u0026amp; Wang \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The program facilitates analysis by assuming a range of potential mating systems, including polygamous and monogamous behaviours in both males and females. Given that mustelids are known for their flexible mating systems\u0026mdash;for example, \u003cem\u003eMustela putorius\u003c/em\u003e exhibits polygyny (Lod\u0026eacute;, 2001), and pine martens have a biological mechanism of delayed implantation that increases their opportunity to mate with multiple males (Yamaguchi \u003cem\u003eet al.\u003c/em\u003e, 2004), we attempted analyses with configurations of both sexes being polygamous, monogamous, as well as with male-only polygamy and female monogamy. This approach is particularly relevant since a strictly polygamous mating system is known to reduce effective population size estimates (Wang \u003cem\u003eet al.\u003c/em\u003e, 2013), and mustelids have an ability to avoid this. We selected a long-length run with five replicates to obtain random and non-random estimated effective population sizes with 95% confidence intervals.\u003c/p\u003e \u003cp\u003eThe program BOTTLENECK v.1.2.02 (Piry et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) was used to detect whether signatures of a genetic bottleneck were present in the Scottish pine marten population. Genetic bottlenecks are detected when there is an excess of heterozygosity in comparison to the number of alleles. The method implemented in the programme relies on the theory that at mutation-drift equilibrium (i.e. the effective size of the population has remained stable in the past), it is equally likely that a locus will exhibit either a heterozygosity excess or deficit. To test if the population contained a significant excess of heterozygosity, we used the three tests available: the \"sign test\", a \"standardized differences test\", and a \"Wilcoxon sign-rank test\", and applied three different models: Infinite Allele Model (IAM), Two Phase Model (TPM) and Stepwise Mutation Model (SMM). For the TPM model we applied the following settings: 80% single-step mutations, a variance among multiple steps of 12, and 5000 iterations. We used a descriptor of the allele frequency distribution (\"mode-shift\" indicator) which discriminates many bottlenecked populations from stable populations (Luikart et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The M-ratio test (Garza \u0026amp; Williamson, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) was used to investigate whether a potential reduction in population size had occurred. If a reduction has occurred in the population size, the number of alleles (k) is expected to decline faster than the range in allele size (r) as most of the alleles that are lost from the population occur within the range, rather than at the edges resulting in a lower M-ratio (k/r). The M-ratio will be \u0026ge;\u0026thinsp;0.8 if a population has not experienced a reduction in population size, while a value\u0026thinsp;\u0026lt;\u0026thinsp;0.7 indicates that a reduction in population size has occurred.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Differentiation\u003c/h2\u003e \u003cp\u003eTo initially investigate the presence of genetic structure, a principal coordinate analysis (PCoA) was generated in GENALEX and visualised using Pandas (McKinney \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Matplotlib (Hunter \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) in Python 3 (Van Rossum \u0026amp; Drake, 2009). To help visualise the samples for genetic differentiation, it was categorised into the six sampling zones defined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe number of genetic clusters (K) present in the population was modelled using the programme STRUCTURE v.2.3.1 (Pritchard et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Falush et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The programme relies on a Bayesian clustering algorithm which we used to analyse the data with default settings and a burn-in period of 100 000, followed by 400 000 replicates with no prior population information. Values of K ranged from one to 10, with each K value replicated five times to assess the most likely number of inferred populations. The most likely K was assessed by calculating the mean likelihood, L(K), and implementing the ΔK method (Evanno et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) using STRUCTURE HARVESTER (Earl \u0026amp; vonHoldt, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in Python 3. The web server CLUMPAK was used to summarize and visualize the STRUCTURE results (Kopelman \u003cem\u003eet al.\u003c/em\u003e, 2015). We then mapped the individuals as before according to their cluster assignment using the folium library (Filipe et al \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Python 3 (Van Rossum \u0026amp; Drake, 2009), and visualised using OpenStreetMap (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.openstreetmap.org/\u003c/span\u003e\u003cspan address=\"https://www.openstreetmap.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) CC BY 3.0.\u003c/p\u003e \u003cp\u003eGenetic differentiation was further evaluated with the software FSTAT version 2.9.3 (Goudet \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) for the sampling locations listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Pairwise F\u003csub\u003eST\u003c/sub\u003e values were generated under 10,000 permutations and a Bonferroni correction for multiple tests was applied. The pairwise F\u003csub\u003eST\u003c/sub\u003e values between them was considered as an index of isolation and a Mantel test was performed on the sampling localities to determine whether there was an overall correlation between geographic distance and genetic divergence (Smouse \u003cem\u003eet al.\u003c/em\u003e, 1986). Using symmetric distance matrices, the Pearson correlation coefficient was calculated between the upper triangular elements of the two symmetric matrices using 10,000 permutations. In each permutation, the entries of one matrix were randomly shuffled, and the Pearson correlation coefficient was recalculated. The p-value was estimated as the proportion of permutations where the recalculated correlation coefficient was greater than or equal to the observed coefficient. Calculations were carried out using Python, with packages NumPy (Van Der Walt \u003cem\u003eet al.\u003c/em\u003e, 2011), SciPy (Virtanen \u003cem\u003eet al.\u003c/em\u003e, 2020), and Matplotlib (Hunter, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Spatial autocorrelation analyses using GenAlEx 6.51b2 with 999 permutations was used to compare the pairwise relationship between genetic and spatial distance between pairs of individuals, and the Pearson correlation was calculated as before.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive population statistics are provided in Table 1. The number of individuals successfully genotyped at each locus ranged from 184 to 206 and averaged 202. The number of alleles averaged 3.5. Levels of expected heterozygosity ranged from 0.449 at Mvi1341 to 0.698 at Mar-64 and averaged 0.567 across all loci. Levels of observed heterozygosity ranged from 0.325 at Mvis075 to 0.634 at Mar-64 and averaged 0.503 across all loci. F\u003csub\u003eis\u003c/sub\u003e (inbreeding coefficient) values ranged from -0.024 at Mvi1341 to 0.346 at Mvis075 and averaged 0.113 across all loci, with five of the 12 loci exhibiting significant deviation from Hardy-Weinberg equilibrium (P = 0.05) and four loci remained significant after Bonferroni correction (P = 0.004) (Table 1). Linkage disequilibrium was detected in 27 of the 66 possible pairwise comparisons at the 5% significance level, and three remained positive following Bonferroni correction (P = 0.0007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Descriptive statistics for pine martens in Scotland across 12 microsatellite loci\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"701\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMel1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMa2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMvi1341\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGg7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMar-21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMar-53\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMel105\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMER041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMar-08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMvis075\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMar-64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMa08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e202.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003csub\u003ea\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.503\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.567\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003csub\u003eis\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.189\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.157\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.164\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.346\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.093\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.113\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations are as follows: N = number of individuals genotyped at each locus, N\u003csub\u003ea\u003c/sub\u003e = number of alleles per locus; H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003e= expected heterozygosity; H\u003csub\u003eo\u003c/sub\u003e = observed heterozygosity; F\u003csub\u003eis\u003c/sub\u003e = inbreeding coefficient, with values in bold indicating significant deviation from Hardy\u0026ndash;Weinberg equilibrium at P = 0.05; and bold values in italics indicating non significance after Bonferroni correction (P = 0.004).\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe number of alleles, expected and observed levels of heterozygosity were also calculated for the six general sampling zones outlined in Figure 1. The highest number of alleles was found in the Highlands (3.3) and the lowest number of alleles was found in Dumfries and Galloway (2.3). H\u003csub\u003eo\u003c/sub\u003e ranged from 0.545 in the Borders to 0.422 in the Cairngorms, and H\u003csub\u003ee\u003c/sub\u003e ranged from 0.546 in the Highlands to 0.423 in the Cairngorms (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Average number of alleles, observed and expected levels of heterozygosity across sampling zones.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003csub\u003ea\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003ee\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003eAberdeen (n = 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003eHighlands (n = 92)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003eCairngorms (n = 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003eCentral (n = 33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003eBorders (n = 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003eD\u0026amp;G (n = 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: D\u0026amp;G = Dumfries and Galloway\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe effective population size (N\u003csub\u003ee\u003c/sub\u003e) calculated using the linkage disequilibrium method was estimated initially to be in between 22.7 (CI 18 \u0026ndash; 28.3) and 23.7 (CI 19 \u0026ndash; 29) using PCrit values of 0.05 and 0.02 which excludes rare alleles from the analysis, which may otherwise further decrease the estimates for effective population size. This analysis was repeated by dividing the data into the later defined STRUCTURE clusters (K = 3), and the results averaged an N\u003csub\u003ee\u003c/sub\u003e of 28.5 and CL values ranged from 21 \u0026ndash; 42.2, providing slightly higher estimates than before, and suggesting that genetic structure had some impact on these estimates. The N\u003csub\u003ee\u003c/sub\u003e as defined by the sibship assignment method facilitated the inclusion of different mating scenarios and the following results were obtained for the following scenarios: both sexes being (a) polygamous (N\u003csub\u003ee\u003c/sub\u003e = 83 (CL 61 \u0026ndash; 116)), (b) monogamous (N\u003csub\u003ee\u003c/sub\u003e = 137 (CL 107 \u0026ndash; 176) , as well as \u0026nbsp;(c) male-only polygamy and female monogamy (N\u003csub\u003ee\u003c/sub\u003e = 97 (CL 74 -128).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll three of the models, IAM, TPM and SMM, and associated tests for significance, indicated the presence of a genetic bottleneck in the population (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e The probability values for the presence of a heterozygosity excess, assuming mutation-drift equilibrium as the null hypothesis, using the Infinite Allele (IAM), Two-phase (TPM) and Stepwise Mutation (SMM) models.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eSign Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eStandardised Difference Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eWilcoxon Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMode Shift\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eIAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eTPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eSMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eIAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eTPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eSMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eIAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eTPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003eSMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe average M-ratio value was under the threshold \u0026lt; 0.7 which indicates that the population has experienced a reduction in population size (average 0.42; range 0.21 \u0026ndash; 1). In this case, ten of the 12 loci used in this study had an M-ratio \u0026lt; 0.7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PCoA (Figure 2), showed some evidence of genetic structure, with individuals from Dumfries and Galloway and the Cairngorms showing some evidence of genetic differentiation, while the majority of the remaining individuals clustered together.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 198 individuals were analysed for population-based assignment. The plot of the mean likelihood, L(K), established from combining each replicate per K value and associated standard deviation from STRUCTURE HARVESTER showed a peak in the dataset at K = 5 (Fig. 3a). The delta K showed that K = 3 was the most likely number of genetic clusters within the population, and a small peak also occurred at K = 5 (Fig. 3A) (Evanno \u003cem\u003eet al.,\u003c/em\u003e 2005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs Figure 3 (a \u0026amp; b) did not conclusively determine the true number of clusters (K), K values from 2 to 5 were examined in Figure 4. The analysis revealed that samples from Dumfries and Galloway formed the first distinct cluster (blue) separating from the dataset at K = 2, with notable admixture observed as animals from the Highlands, Central, and Borders regions also aligned with this blue cluster. The Cairngorms were predominantly represented in the second cluster (orange), which also included a significant proportion of animals from Aberdeenshire, the Highlands, Central, and Borders. At K = 3, most of the Dumfries and Galloway samples distinctly segregated into a third cluster (purple), with a few animals from the Highlands (n = 3), Central (n = 1), and Borders (n = 2) showing affinity for this cluster. Interestingly, two animals from Dumfries and Galloway were identified with the blue cluster, primarily associated with the Highlands. By K = 4, the Cairngorms distinctly emerged as a separate cluster, yet shared similarities with groups from Aberdeen, the Highlands, Central, and Borders. The blue cluster continued to be dominant in the Highlands, with a substantial number of animals from Aberdeen, Central, and Borders, including two from Dumfries and Galloway, also falling into this cluster. Additionally, at K = 4, a new cluster (green) appeared, mainly within the Highlands and among a few individuals in other regions, excluding the Cairngorms. This cluster was less defined, except for several Highland animals with strong assignments. Finally, at K = 5, another cluster (purple) was most distinct in the Highlands, with a few animals in Central and Borders also exhibiting a connection to this cluster. The Cairngorms, Central (partial) and Dumfries and Galloway formed the most distinct clusters at K = 5. Animals from Aberdeen, Highlands, Borders and parts of Central all contained high levels of admixture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003eis\u003c/sub\u003e was calculated for clusters defined at K = 3 and only one cluster had a moderately F\u003csub\u003eis\u003c/sub\u003e at 0.068 and remained significant following Bonferroni correction (P = 0.0014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the presence of genetic structure was evident in the Cairngorms and Highlands, K = 3 was mapped to help understand if geographic barriers influenced the structure present (Figure 5).\u003c/p\u003e\n\u003cp\u003eSignificant pairwise F\u003csub\u003est\u003c/sub\u003e values varied, with the highest being 0.236 between the Highlands and the Cairngorms, and the lowest significant value at 0.044 between Dumfries and Galloway and Central. Conversely, low and nonsignificant F\u003csub\u003est\u003c/sub\u003e values were observed between Aberdeen and Central, between Borders and Dumfries and Galloway, and between Dumfries and Galloway and the Scottish Borders, indicating minimal genetic differentiation in these pairs (Table 4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Pairwise F\u003csub\u003est\u0026nbsp;\u003c/sub\u003evalues across sampling sites, with significant differences bolded for emphasis. P-values have been adjusted using a Bonferroni correction, setting the significance cut-off at P = 0.003 to reduce the risk of false positive results in the dataset.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"496\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.18548387096774%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003eAberdeen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003eHighlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.95967741935484%\" valign=\"bottom\"\u003e\n \u003cp\u003eCairngorms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\n \u003cp\u003eBorders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.18548387096774%\" valign=\"bottom\"\u003e\n \u003cp\u003eHighlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.187\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.95967741935484%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.18548387096774%\" valign=\"bottom\"\u003e\n \u003cp\u003eCairngorms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.192\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.236\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.95967741935484%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.18548387096774%\" valign=\"bottom\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.169\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.95967741935484%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.134\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.18548387096774%\" valign=\"bottom\"\u003e\n \u003cp\u003eBorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.186\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.95967741935484%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.179\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.072\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.18548387096774%\" valign=\"bottom\"\u003e\n \u003cp\u003eD\u0026amp;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.524193548387096%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.219\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.95967741935484%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.179\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe Mantel test comparing F\u003csub\u003est\u003c/sub\u003e and geographic distances between sampling localities revealed a weak, negative correlation (r = -0.071, P = 0.603), indicating no significant link between genetic differentiation and geographic distances among sampled areas. Conversely, a separate Mantel test on genetic and geographic distances between individuals showed a weak, positive correlation (r = 0.0297, p = 0.00003), marking a significant, but very small, association between genetic and geographic distances across individuals.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we aimed to assess the genetic diversity of the contemporary Scottish pine marten population using microsatellites. Our goal was to gain insights into the impact past declines have on the current population, which is steadily expanding and recolonising its historical range. This is crucial for informing future conservation management and supporting the ongoing recovery of the species in Britain.\u003c/p\u003e\n\u003cp\u003eIn this study, genetic diversity was assessed using a panel of 12 microsatellite markers. These markers, carefully selected and optimised over time for their variability and ability to amplify pine marten DNA from poor-quality samples, such as hair from non-invasive genetic studies (e.g. Mullins \u003cem\u003eet al.,\u003c/em\u003e 2010; Croose \u003cem\u003eet al.,\u003c/em\u003e 2019; Twining \u003cem\u003eet al.,\u003c/em\u003e 2022), are arguably biased towards this variability. Although microsatellite markers are a cost-effective and reliable method for assessing genetic diversity in various species, comparisons with SNP data from restriction-site-associated DNA sequencing (RADseq) suggest that SNPs may provide deeper historical population insights. This is due to their slower mutation rate compared to microsatellite regions, making them particularly useful in small, isolated populations with low diversity. Indeed, many newer studies, some of which are discussed below, take a genomic approach to provide genetic diversity assessments of recovering species such as the wolf (\u003cem\u003eCanis lupus\u003c/em\u003e) (vonHoldt \u003cem\u003eet al.,\u003c/em\u003e 2023). While the cost implications and overall benefits of transitioning from a well-developed microsatellite panel should also be considered (Lemopoulos \u003cem\u003eet al.,\u003c/em\u003e 2019; Hauser \u003cem\u003eet al.,\u003c/em\u003e 2021), a genomic approach could be considered for future monitoring of the pine marten. The sampling method employed in this study was ad hoc, and some samples, now 10 years old, provide a genetic snapshot that is likely out-of-date, given the rapid expansion of the species. However, this study could serve as a valuable foundation for future studies, which should aim for more systematic sampling across the species\u0026apos; range.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of nuclear genetic diversity, the number of alleles across the selected microsatellite loci in the Scottish population averaged 3.5, with H\u003csub\u003eo\u003c/sub\u003e and H\u003csub\u003ee\u003c/sub\u003e averaging 0.503 and 0.567. These figures are slightly lower than the values reported for the pine marten in Ireland, where the number of alleles averaged 4.3 and observed and expected levels of heterozygosity averaged 0.547 and 0.549, respectively (O\u0026rsquo;Reilly \u003cem\u003eet al.,\u003c/em\u003e 2021). Only the expected levels of heterozygosity are slightly higher in the Scottish population. O\u0026rsquo;Reilly \u003cem\u003eet al.,\u003c/em\u003e (2021) used 11 out of 12 of the same microsatellite markers, making comparisons reliable across these two studies. Similar levels of genetic diversity were noted in pine marten populations recorded in Spain (Ruiz-Gonz\u0026aacute;lez \u003cem\u003eet al.,\u003c/em\u003e 2014), Denmark (Pertoldi \u003cem\u003eet al.,\u003c/em\u003e 2008), and France (Mergey \u003cem\u003eet al.,\u003c/em\u003e 2012). In North America averages of H\u003csub\u003eo\u0026nbsp;\u003c/sub\u003eand H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003ewere similar for \u003cem\u003eM. americana\u003c/em\u003e (H\u003csub\u003eo\u003c/sub\u003e = 0.57, H\u003csub\u003ee\u003c/sub\u003e = 0.58) and \u003cem\u003eM. caurina\u003c/em\u003e (H\u003csub\u003eo\u0026nbsp;\u003c/sub\u003e= 0.57, H\u003csub\u003ee\u003c/sub\u003e = 0.56) (Lucid \u003cem\u003eet al.,\u003c/em\u003e 2020), suggesting that the results obtained in this study are similar across the range of this species and similar species\u0026rsquo; ranges.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, genetic diversity in the Cairngorms (H\u003csub\u003eo\u003c/sub\u003e = 0.44, H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003e= 0.42) and Dumfries and Galloway (H\u003csub\u003eo\u003c/sub\u003e = 0.45, H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003e= 0.44) was lower than average. This was anticipated in Dumfries and Galloway, where 12 pine martens were translocated from the Highlands in the 1980s, likely leading to genetic isolation. Genetic differentiation tests confirmed this region\u0026apos;s uniqueness, as indicated by its early separation in the STRUCTURE analysis and differentiation in the PCOA. Significant F\u003csub\u003est\u003c/sub\u003e values further underscored this isolation, particularly when compared with other sampled zones. However, these values decreased closer to the Scottish Borders and Central Scotland, indicating some gene flow. Surprisingly, the highest F\u003csub\u003est\u003c/sub\u003e values were observed with the Highlands, the source of the translocation, suggesting genetic drift has made this population genetically distinct. STRUCTURE plots revealed admixture in some individuals at K = 3, indicating genetic mixing between animals from Dumfries and Galloway, Highlands, Central and Borders, but it remains unclear whether this reflects past or more recent gene flow. The genetic isolation observed in the Cairngorms, also supported by differentiation tests, may be attributed to geographical barriers, such as high terrain, the River Spey, and several lochs, alongside the presence of the A9 roadway, causing further isolation. Indeed, the rugged terrain of the Scottish Highlands, characterised by mountainous landscapes, numerous lochs (lakes), deep glens (valleys), and expansive moorlands, might have contributed to the fragmentation of species across Scotland. This fragmentation could have led to the formation of distinct genetic clusters, which are now mixing as individuals recolonise Scotland, producing a genetically mixed population in many areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results, analysed at individual level, suggest evidence of very weak isolation by distance within the study population, with geographically closer individuals tending to be more genetically similar. There was no association between genetic differentiation and geography when we analysed the data according to sampling zone, and it is likely that most of the genetic differentiation is influenced by other factors, which could include reintroductions, opportunistic ad hoc sampling selection, migration, genetic drift, or non-random mating patterns. Consequently, it is unclear how this genetic fragmentation of the population occurred. Beyond the known translocation to Dumfries and Galloway, there have been unofficial releases in the Scottish Borders, and possibly more that have gone undocumented, as evidenced by reports of unofficial introductions on the Isle of Mull (Solow \u003cem\u003eet al.,\u003c/em\u003e 2013). Such undocumented actions could have contributed to the observed levels of genetic mixing in this study. It is also likely \u0026nbsp;that the genetic bottlenecks and inbreeding detected in our study have been influenced by unofficial translocations involving small numbers of animals. This could potentially create multiple, relatively recent founder effects in the population, thus complicating the use of conservation genetics to infer adaptive management strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFive out of the 12 loci exhibited significantly positive F\u003csub\u003eis\u003c/sub\u003e values. While positive F\u003csub\u003eis\u003c/sub\u003e values can indicate inbreeding, they are also associated with genetic admixture, which is clear in this case. Analysing populations with underlying genetic structure for genetic diversity can artificially increase the level of homozygotes at the expense of heterozygotes, a phenomenon perceived as inbreeding. This is known as the Wahlund effect, which occurs when a population, composed of two or more subpopulations, is analysed as if it were a single, homogeneous population (Waples, 2015). However, in this study, defining populations for analysis was not straightforward, as most sampled localities exhibited genetic structure or sub structure that could not be easily separated for analysis. Indeed, it is rare in population genetics that \u0026apos;ideal\u0026apos; populations are ever sampled from a statistical viewpoint. Therefore, the results should be interpreted with this consideration in mind (De Mee\u0026ucirc;s, 2018). A study of European rabbit populations across 17 sites in the East Anglian region of Britain, following a population crash due to a myxomatosis outbreak, found that the populations became genetically distinct with low effective population sizes. It suggested that this genetic divergence resulted from the myxomatosis-induced crash, combined with reproductive and social characteristics that influenced the genetics rather than from past historical events (Surridge \u003cem\u003eet al.,\u003c/em\u003e 1999). It is possible that a similar scenario exists in this study, where the pine marten population may retain genetic signatures of past population declines. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was evidence of a genetic bottleneck across all tests implemented in this study. This was also confirmed by the mode-shift test. It is important to note that the Wahlund effect can confound these patterns, making it challenging to distinguish between the loss of alleles due to a bottleneck and the artificial reduction in heterozygosity due to population substructure. However, the mode-shift test is designed to detect shifts in the distribution of allele frequencies indicative of a bottleneck, making it less influenced by the presence of the Wahlund effect, but the results should be viewed with this implication. However, some evidence of a genetic bottleneck was also found in the Irish pine marten population, which was not impacted by genetic structure (O\u0026rsquo;Reilly \u003cem\u003eet al.,\u003c/em\u003e 2021). The mode shift test also indicated a shifted mode, which did not conform to the L-shaped distribution expected to occur in a non-bottlenecked population. The observed shift is associated with the loss of low-frequency alleles due to a decline in the Scottish pine marten population size, signifying a genetic bottleneck. When a population undergoes a bottleneck, there is a distinct mode-shift distortion in allele frequencies. Specifically, alleles with a low frequency (less than 0.1) become rarer compared to those in an intermediate range (e.g. 0.1\u0026ndash;0.2). An L-shaped distribution suggests no recent bottlenecks for several generations; but a shifted mode, as seen in this study, signifies a recent genetic bottleneck (Luikart \u003cem\u003eet al.,\u003c/em\u003e 1998; Piry \u003cem\u003eet al.,\u003c/em\u003e 1999).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilar genetic bottlenecks were not detected, when previously tested for, in France (Mergey \u003cem\u003eet al.,\u003c/em\u003e 2012) or Sardinia (Coli \u003cem\u003eet al.,\u003c/em\u003e 2011), but Ruiz-Gonzalez et al., (2015) did find significant support for historical reductions in effective population sizes in the north of Spain, which was attributed to habitat fragmentation and the presence of a competing mustelid, \u003cem\u003eMartes foina\u003c/em\u003e, but there were also unknown factors that may have contributed to a reduction of gene flow. \u0026nbsp;For example, it was proposed that the pine marten was expanding its range in parts of northern Spain, and a lag time, akin to what this study proposes, may have hindered landscape features from being reflected in the species\u0026apos; genetic structure. The detection of a potential bottleneck signature in the present study could reflect the pine marten\u0026rsquo;s past population retraction, as these signatures can remain for one hundred years or more, even in variable loci like microsatellites. Regardless of the direct cause of the result in this study, be it a past bottleneck or the impact of genetic structure, increasing contemporary gene flow within Scotland would benefit the diversity of the species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe effective population size estimates from this study ranged from depressed (less than 50), as determined by the linkage disequilibrium method, to modest (less than 200), as determined by the sibship method, all falling below the putative 500-threshold required for long-term viability (Frankham \u003cem\u003eet al.,\u003c/em\u003e 2014). The variation in results can be attributed to the different underlying assumptions of the methods used to generate these estimates. The linkage disequilibrium method, which assumes observed linkage disequilibrium originates solely from genetic drift in a unified population, may underestimate the effective population size. This underestimation can occur due to the Wahlund effect, which inflates linkage disequilibrium and could misinterpret the extent of population size or drift in structured populations (Waples and Do, 2010). This scenario is very likely in our study, given the underlying genetic structure across most of Scotland. Conversely, the higher estimate derived from the sibship method offers a more reliable means for deriving effective population size estimates in cases like ours, where samples are not collected systematically and may include related individuals across different generations. Less impacted by the Wahlund effect compared to the linkage disequilibrium method, the sibship method focuses on identifying full and half-sibling groups within a sample based on genetic similarity among individuals. This approach avoids reliance on population-level allele frequencies or linkage disequilibrium patterns, making it more reliable for our study. Waples (2021) found that the linkage disequilibrium method outperformed the sibship method in terms of precision, but Gilbert \u0026amp; Whitlock, (2015) also warned that the accuracy of any method is dependent on the demography of the species. What we can say in this case is that the effective population was found to be modest using two independent methods, something which Waples (2021) says improves the overall precision of the estimates.\u003c/p\u003e\n\u003cp\u003eEstimates for effective population size for \u003cem\u003eMartes americana\u003c/em\u003e and \u003cem\u003eM. caurina\u003c/em\u003e, also derived using the same linkage disequilibrium method, were also generally modest (in the hundreds) across a 53,474 km\u003csup\u003e2\u003c/sup\u003e area, encompassing portions of British Columbia, Idaho, Montana, and Washington (Lucid \u003cem\u003eet al.,\u003c/em\u003e 2020), and suggested that the long-term protection of the species in the region depended on corridor conservation efforts. Similar estimates were derived for the recovering pine marten in Ireland (O\u0026rsquo;Reilly \u003cem\u003eet al.,\u003c/em\u003e 2021). In Finland, the wolverine (\u003cem\u003eGulo gulo\u003c/em\u003e) is showing recovery, especially in the north and east, thanks to translocations in the late 20th century (Lansink \u003cem\u003eet al.,\u003c/em\u003e 2020). Two primary clusters displayed limited gene flow, with heterozygosity levels of H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003e= 0.49 and H\u003csub\u003ee\u003c/sub\u003e = 0.57, and average alleles of 3.8 and 4.0 \u0026ndash; but the effective population size was under 50, indicating previous genetic bottlenecks and future viability concerns (Frankham \u003cem\u003eet al.,\u003c/em\u003e 2014). \u0026nbsp;It appears that estimates of low to modest effective population size below 500 are not uncommon in mustelid species, particularly those that have had past and current population stresses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor species whose populations have been anthropogenically reduced, such as the grey wolf, and are now expanding, the effective population size can be significantly smaller than the census population count. In 2021, the northern Rocky Mountains had a census size estimated at 3,354, and the western Great Lakes at 4,526, but when these values were converted to an effective population size, they ranged between 201 and 335 wolves for the northern Rocky Mountains, and between 272 and 453 for the western Great Lakes. Expressing the census population as a multiple of the effective population size reveals that the census population of grey wolves is estimated to be between 10 and 18 times larger than its effective population size. Given the strong skew in the effective-to-census size ratio in grey wolves, conservation practitioners aim to maintain larger wolf populations to ensure long-term adaptation and survival (vonHoldt \u003cem\u003eet al.,\u003c/em\u003e in 2023). However, achieving the required number is challenging, considering that the species\u0026apos; dispersal capabilities and success must be considered. To maintain and increase effective population size, vonHoldt \u003cem\u003eet al.,\u003c/em\u003e (2023) recommend that dispersers be granted protection. Indeed, there are many parallels in the pine marten population in this study, as this species was historically heavily persecuted and is now expanding. However, migrants too face barriers to dispersal, particularly roads. Based on the census estimate of 3,700 (Mathews \u0026amp; Harrower, 2020) and the effective population sizes derived from sibships, ranging from 83 to 137, the ratio of the census population to the effective population size in pine martens is estimated to be between 27 and 44 times larger. \u0026nbsp;The strong skew in the effective-to-census size ratio in pine martens also implies that larger populations are necessary to ensure long-term adaptation and survival, as is the case with wolves. The higher multiplier in pine martens compared to grey wolves may stem from challenges encountered in accurately estimating effective population sizes as previously discussed, \u0026nbsp;and could also reflect variations in social organisation, complicating cross-study comparisons. vonHoldt \u003cem\u003eet al.,\u003c/em\u003e (2023) recommended that studies should be carried out periodically to reassess the situation, which could also be applied in this case. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the uncertainty raised in this study related to the genetic recovery of the pine marten, which appears to be lagging behind its national demographic recovery, it is timely to suggest that long-term genetic population monitoring be adopted for the species throughout Britain including the re-establishing \u0026nbsp;populations in England and Wales. Notably, such monitoring is already being planned for the restored population in Wales by Vincent Wildlife Trust (VWT) (J. MacPherson pers. comm.). In our experience, methods involving the collection of hair samples using tubes or feeders have consistently yielded good-quality DNA suitable for genotyping, as demonstrated in this study. Source populations in Scotland should undergo similar checks every decade. The feasibility of implementing a nationwide and standardised approach to long-term population and genetic monitoring should be evaluated and discussed among stakeholders. Methods utilised by Sheehy et al. (2018) in Scotland and O\u0026rsquo;Mahony et al. (2017a) in Ireland, along with density modelling techniques combined with genotyping as outlined in Twining et al. (2022), offer promising frameworks for such nationwide initiatives. By doing so, we can ensure the health and sustainability of these populations, using this study as a foundational reference for future efforts, and plan adaptive interventions accordingly. When effectively carried out, these efforts could shape future policy, legislation, and guidelines concerning the restoration of wildlife species. This precautionary approach seems wise considering the impacts of global heating and climate breakdown upon pine martens and the habitats upon which they depend. Given the parallels within Britain and Ireland (O\u0026rsquo;Reilly \u003cem\u003eet al.,\u003c/em\u003e 2021), there is an opportunity to collaborate on understanding the recovery of the pine marten. This collaboration could inform and establish best practices for other conservation recovery projects, especially as the field of genetic reinforcement is still in its early stages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Relevant funding sources related to sample collection and molecular analysis were previously declared in previously published cited material in the present study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOR, ES, JMacP and DOM conceived the study. COR carried out the molecular laboratory work. COR and DOM analysed the data and wrote the initial draft of the paper, and all authors contributed to subsequent drafts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated and analysed during the current study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge Dr. Nicholas Aebischer for his valuable comments pertaining to the discussion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBirks JDS (2020) Pine Martens. 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Evol Appl 3:244\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1752-4571.2009.00104.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1752-4571.2009.00104.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Conservation, Management, Restoration, Population Genetics, Microsatellites, Martes martes","lastPublishedDoi":"10.21203/rs.3.rs-3997852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3997852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigated the genetic diversity of the contemporary Scottish pine marten population using neutral microsatellite markers, sampling 206 individuals across an area of almost 32,000 km\u003csup\u003e2\u003c/sup\u003e. Our results revealed that the genetic diversity in the Scottish population is modest with the levels of observed and expected heterozygosity ranging from the Highlands (H\u003csub\u003eo\u003c/sub\u003e 0.52, H\u003csub\u003ee\u003c/sub\u003e 0.55) to the Cairngorms (H\u003csub\u003eo\u003c/sub\u003e 0.44, H\u003csub\u003ee\u003c/sub\u003e 0.42), and the number of alleles ranged from 3.3 in the Highlands and Central to 2.3 in Dumfries and Galloway, but there were high levels of genetic admixture across the country, some of which may be attributed to natural demographic recovery from previously isolated refuges, and unofficial translocations have also influenced the genetic mixing evident in the population today. Genetic sub structuring, resulting in the Wahlund effect, complicated evaluations of diversity, effective population size, and bottlenecks, and commonly used linkage disequilibrium methods for estimating effective population size yielded improbably low figures. A less commonly used method relying on sibship proved more resilient to the effects of genetic sub structuring, but still yielded estimates under 200, below the viability threshold for long-term population survival. Despite demographic expansion, genetic recovery lagged, suggesting the need for increased gene flow through wildlife corridors.\u003c/p\u003e","manuscriptTitle":"Genetic Lag in a Demographically Recovering Carnivore: The Case of the British Pine Marten (Martes martes)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 17:06:53","doi":"10.21203/rs.3.rs-3997852/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-29T15:45:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-12T20:20:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93310643652386283134145303040042426231","date":"2024-06-14T07:01:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0a85404a-3504-4cb3-895d-939e65a6a581","date":"2024-03-21T18:38:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9e6ca1b4-97cc-4bb2-a073-980be0f209dc","date":"2024-03-12T17:37:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-12T16:52:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-02T06:37:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-02T06:37:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2024-02-28T21:47:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cabee7fd-b4fe-4860-af5a-70b784643068","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:03:06+00:00","versionOfRecord":{"articleIdentity":"rs-3997852","link":"https://doi.org/10.1007/s10592-024-01660-4","journal":{"identity":"conservation-genetics","isVorOnly":false,"title":"Conservation Genetics"},"publishedOn":"2024-11-23 15:57:48","publishedOnDateReadable":"November 23rd, 2024"},"versionCreatedAt":"2024-03-05 17:06:53","video":"","vorDoi":"10.1007/s10592-024-01660-4","vorDoiUrl":"https://doi.org/10.1007/s10592-024-01660-4","workflowStages":[]},"version":"v1","identity":"rs-3997852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3997852","identity":"rs-3997852","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}