Population-genetic structure of the hazel dormouse Muscardinus avellanarius in the Meuse-Rhine Euregion

Population-genetic structure of the hazel dormouse Muscardinus avellanarius in the Meuse-Rhine Euregion | 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 Population-genetic structure of the hazel dormouse Muscardinus avellanarius in the Meuse-Rhine Euregion Alice Mouton, Goedele Verbeylen, Pim Lemmers, Maurice La Haye, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6545691/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The survival of many species has been impacted dramatically by human activities over the last few centuries. More specifically, forest fragmentation, due to human settlement and development of agriculture, has highly affected arboreal species. Understanding patterns of genetic structure of endangered species occupying fragmented forest habitats is a requirement for appropriate conservation management, especially for species with low dispersal abilities like the hazel dormouse ( Muscardinus avellanarius ). The hazel dormouse is an arboreal rodent that is strictly protected in Europe. The aim of this study conducted from 2004 to 2011 was to investigate population fragmentation of the hazel dormouse in part of the Meuse-Rhine Euregion using microsatellite markers. The results revealed a significant genetic differentiation and low gene flow between subpopulations. Fragmentation of the hazel dormouse population in this region seems to have occurred during the 20th century, suggesting that active management to improve habitat quality, amount of habitat and connectivity is essential to impede future genetic erosion. Furthermore, our results show that railway verges, like other linear habitat elements, cannot only be permanently inhabited, but also act as a valuable corridor, connecting subpopulations and allowing the colonisation of new sites. Finally, in response to our study, both Flemish and Dutch hazel dormouse species protection plans included the objective of providing a functional ecological corridor between several of the subpopulations, which has already been partially implemented. arboreal rodent conservation management Gliridae habitat fragmentation microsatellite marker Figures Figure 1 Figure 2 Figure 3 Introduction Spatial organisation of wildlife populations and their genetic diversity is impacted by habitat loss and fragmentation (Coster and Kovach 2012 ; Kuemmerle et al. 2012 ; Hindrikson et al. 2013 ; Mona et al. 2014 ). With reduced movement or gene flow between habitat patches, fragmentation can effectively reduce population sizes, thereby increasing genetic drift and the risk of inbreeding depression, especially in arboreal species (Bijlsma and Loeschcke 2012 ; Fietz et al. 2014 ; Bani et al. 2018 ; Friebe et al. 2018 ). Population structure and patterns of genetic diversity might also be influenced by past anthropogenic activities. Alteration of the landscape is a process that started since the establishment of the first agricultural societies during the Stone Ages in the mid-Holocene and then grew larger during the subsequent Bronze, Iron, Roman and Middle Ages (Ruddiman 2003 ; Kaplan et al. 2009 ). Changes in agricultural practice have influenced forest composition and structure through deforestation, slash-and-burn agriculture and clearing (Eriksson et al. 2002 ; Bradshaw 2004 ). It can take many generations for changes in level of gene flow or genetic drift to be translated into detectable effects on genetic structure in mammals (Keyghobadi 2007 ). The influence of human civilisation on geographical distribution and population density of European arboreal species such as edible dormouse ( Glis glis ) and hazel dormouse ( Muscardinus avellanarius ) was suggested by Carpaneto and Cristaldi ( 1994 ) through a historical and biogeographical analysis, supported by paleontological data. A genetic study on the edible dormouse across its distribution supported this hypothesis and showed that the current structure of this species is a result of past anthropogenic forest fragmentations that occurred from the Holocene to the Middle Ages (Michaux et al. 2019 ). In this context, we presume that the hazel dormouse might be an appropriate study species for testing whether an effect of past anthropogenic activities on genetic structure is present in the Meuse-Rhine Euregion. The hazel dormouse is a glirid species typical of forested habitats, thriving best in the densest parts – such as well-developed forest edges and canopies, hedgerows and early successional shrub habitats – with a high diversity in seed-and-fruit-bearing plant species (Ehlers 2012 ; Juškaitis and Büchner 2013 ; Goodwin et al. 2018 ). Hazel dormice usually overwinter solitary and on forest floors, not far from the forest edge (Verbeylen et al. 2017 ; Gubert et al. 2023 ). Besides providing valuable habitat, dense, varied hedgerows and hedges also act as corridors for dispersal. Radio-tracking carried out in the UK indicated that hazel dormice were almost completely arboreal and reluctant to cross open ground during their normal nocturnal activity (Bright and Morris 1992 ). However, other studies showed that they can cross open fields over a distance of up to 500 m (Büchner 2008 ; Mortelliti et al. 2013 ; Lemmers et al. 2022 ) and up to 30 m wide roads (Chanin and Gubert 2012 ; Verbeylen et al. 2017 ; Lemmers et al. 2022 ), and even have home ranges spanning both sides of railways, railway bridges and railway service roads (Verbeylen et al. 2015 , 2017 ). Vulnerable to habitat loss and disruption of the structural connectivity provided by hedgerows (Capizzi et al. 2002 ; Mortelliti et al. 2009 , 2013 ), the hazel dormouse is listed under the Annex IV of the European Union Habitats Directive and Annex III of the Bern Convention. Genetic studies on hazel dormouse populations have been conducted in the UK, Ireland, Italy, and Germany (Mills 2012 ; Naim et al. 2012 ; Glass et al. 2015 ; Bani et al. 2017 , 2018 ; Friebe et al. 2018 ). These studies demonstrated that habitat fragmentation negatively impacts the genetic structure of the species and therefore the long-term survival of populations. Conservation plans (e.g. conservation and improvement of habitat quality, amount and connectivity, reintroduction, breeding programmes) have been prepared and are being implemented in the north-western part of the species range where populations have decreased dramatically (Foppen et al. 2002 ; Kuijsten and Krekels 2013 ; Schulz et al. 2013 ; Nijs and Verbeylen 2017 ; Dietz et al. 2018 ; Lemmers et al. 2019 ; Verbeylen et al. 2020 ; Bullion et al. 2025 ). In order to gain more understanding about the impact of habitat fragmentation on genetic diversity and population structure of the hazel dormouse in the Meuse-Rhine Euregion (located at the tripoint of Belgium, the Netherlands and Germany), this study was conducted with the following aims: (1) to assess and describe the genetic diversity and population structure in the cross-border hazel dormouse population using microsatellite markers, and (2) to discuss the implications of these results for the development of a sustainable cross-border hazel dormouse population. Material and methods 1) Study area In Flanders (Belgium), the hazel dormouse used to be distributed in most provinces (West Flanders, East Flanders, Flemish Brabant and Limburg), but strongly declined from the second half of the 20th century. The whole Flemish population seems to be restricted now to the eastern part of the municipality of Voeren (province of Limburg) where the forests inhabited by the hazel dormouse stretch into the Netherlands, Wallonia (Belgium) and Germany (Verbeylen et al. 2017 ). In Wallonia, the hazel dormouse is found mainly south of the rivers Sambre and Meuse with few observations in the north of the region (Schockert et al. 2007 ). In the Netherlands, the species is only found in the southernmost part of the country. It used to be present in a larger part of the province of Limburg, but the distribution declined from the second half of the 20th century. The occurrence is currently limited to the south-easternmost part of the province, but numbers are increasing (Lemmers et al. 2024 ). In the Meuse-Rhine Euregion, the species’ presence was confirmed in a series of Walloon and German forests adjacent to the Flemish and Dutch hazel dormouse areas (Verbeylen and Kuijsten 2013 ). 2) Sampling From 2004 to 2011, a total of 232 hazel dormouse DNA samples were collected in the Meuse-Rhine Euregion. Small tufts of hairs were collected from animals trapped in nest boxes and nest tubes using tweezers and stored immediately in ethanol. Individuals were grouped into four putative subpopulations (Fig. 1 ). The forest names and sampling locations in the study area are available in Supplementary File 1. Sampling location 1 (hereafter SL1, n = 11) comprises individuals from Vrouwenbos-Stroevenbos-Sint-Gillisbos (Voeren, Flemish Limburg, Belgium), sampling location 2 (SL2, n = 124) was located in Broekbos, Konenbos, Veursbos-Roodbos-Vossenaerde and the western railway verges (Voeren, Flemish Limburg, Belgium), sampling location 3 (SL3, n = 63) groups individuals from Teuvenerberg-Gulpdal-Obsinnich and the eastern railway verges (Voeren, Flemish Limburg, Belgium) and from Bovenste Bosch, Kruisbosch, Groote Bosch and Schweibergerbosch (Dutch Limburg, the Netherlands). Individuals from sampling location 4 (SL4, n = 32) originated from Vijlenerbossen, Schimpersbos and Preusbos (Dutch Limburg, the Netherlands) and Preuswald and Aachenerwald (Germany, and Wallonia, Belgium). The samples from Voeren (Flanders, Belgium, n = 186) were collected in the frame of a CMR-study by the Mammal Working Group of Natuurpunt Studie. The remaining 46 samples were collected during the Interreg project ‘Interreg IV Maas-Rijn Habitat Euregio’ (subproject ‘recovery plan hazel dormouse’). The Dutch Mammal Society and Natuurbalans collected samples from 37 individuals present in nest tubes in the Netherlands. Natuurpunt Studie collected 9 samples from individuals occupying shrub nests (5 in Wallonia, Belgium, 3 in Germany and 1 in the Netherlands). DNA extractions with controls were carried out using a QIAmp DNA Micro kit (Qiagen) following manufacturer's instructions. All samples were handled using sterile disposable materials. 3) Microsatellite genotyping Eleven successfully amplified loci (five after Naim et al. 2009 : mavE3, mavB5, mavG3, mavG6, mavA5, and six from Mills et al. 2013 : Mav03, Mav021, Mav032, Mav036, Mav051, Mav040) were combined into multiplex sets according to their size and fluorescent label and subsequently amplified via multiplex polymerase chain reactions (PCR) in a Mastercycler ep Gradient (Eppendorf). The multiplex PCR samples contained 1 µl DNA, 5 µl of Multiplex PCR MasterMix (Qiagen), 0.2 µM of each primer and deionised water to the final volume of 10 µl. Cycling conditions included an initial step at 95°C for 15 min, followed by 35 cycles of denaturation at 94°C for 30 s, annealing at 60°C for 90 s, and extension at 72°C for 30 min. 2 µl of PCR product was mixed with 0.3 µl of Liz GS500 dye (Applied Biosystems) and 12 µl of Hi-Di formamide and loaded onto an ABI 3130 Genetic Analyzer at the University of Brussels. The yielded DNA fragments were analysed using GeneMapper v.4.1 software (Applied Biosystems). Data was converted into STRUCTURE and Genepop format using Convert (Glaubitz 2004 ), into Fstat DAT format using Genetix (Belkhir 2004 ) and into Immanc for BA3 using Formatomatic (Manoukis 2007 ). Around 25 percent of the samples were collected twice, and the amplification was therefore carried out multiple times on a subset of samples. 4) Population structure Only one sample was used per individual and samples with more than ten percent of missing data were removed, leading to a final dataset of 172 samples (SL1: n = 9, SL2: n = 88, SL3: n = 50, SL4: n = 25). Data with sample name, country, sampling location and genotypes are available in Supplementary File 2. The frequency of null alleles was estimated per locus and per sampling location using the expectation maximum algorithm (EMA) implemented in FreeNA with 1000 replications (Chapuis and Estoup 2007 ). We used several approaches to estimate the population structure so that an informative K could be chosen (Janes et al. 2017 ). First, we used a Bayesian assignment as implemented in STRUCTURE v.2.4 (Pritchard et al. 2000 ). Ten iterations were run for each value of K from one to ten using an admixture model, correlated allele frequencies and with a burn-in of 1 x 10 5 and Markov chain Monte Carlo (MCMC) values of 1 x 10 6 . LOCPRIOR models were used with the original sample location to incorporate sampling location without forcing structure. The results were analysed with STRUCTURE Harvester (Earl and von Holdt 2012 ) to identify the optimal number of clusters. A visual output of the STRUCTURE results was generated using Clumpack (Kopelman et al. 2015 ). Then we used a Discriminant Analysis of Principal Components (DAPC) and a Factorial correspondence analysis (FCA) implemented in R v.3.6 (R Core Team 2019 ) with the adegenet package (Jombart et al. 2010 ) and in GENETIX v.4.05.2 (Belkhir 2004 ) respectively as these methods do not make prior assumptions about the population structure model (e.g. HW and gametic equilibrium not assumed) and work well with sample unevenness. We used the function ‘find.clusters’ to identify clusters and then ‘dapc’ to describe the relationships between the clusters using the sampling locations as prior information. DAPC was run using the sampling locations as prior information and then using the genetic structure from the several selected K in STRUCTURE. To estimate contemporary gene flow (within the past one to three generations) among the genetic clusters, we used the Bayesian approach as implemented in the programme BA3 v.3.0.4 (Wilson and Rannala 2003 ). As recommended in the user manual, mixing parameters (allele frequencies (a), inbreeding (f), and migration rate (m)) were all varied until acceptance rates were ~ 30–40%. Once mixing rates were optimised (a = 0.3, f = 0.4 for K = 3), five independent runs with varying random seed numbers (100, 10 (default), 4,656, 246, 25) were performed using 1 x 10 7 iterations, a burn-in of 1 x 10 6 and a sampling frequency of 1,000. The programme TRACER v.1.7.1 (Rambaut et al. 2018 ) was used as a method to qualitatively assess MCMC convergence. 5) Genetic diversity Linkage disequilibrium (LD) was calculated in FSTAT v.2.9.3.2 (Goudet 2001 ) with 10,000 permutations. We used the function ‘divBasic’ and ‘fastDivPart’ in the diveRsity package (Keenan et al. 2013 ) in R v.3.6 (R Core Team 2019 ) to calculate genetic diversity indices such as allelic richness (ar), expected (He) and observed (Ho) heterozygosity, and F-statistics (pairwise FST, DJost, Gst and Fis). 6) Population divergence and history We tested ten different demographic scenarios using the ABC framework implemented in DIYABC v.2.1 (Cornuet et al. 2014 ). We tested whether the main identified clusters originated from the fragmentation of an ancestral panmictic population or if one of them originated from an isolated subpopulation, and whether admixture events occurred between clusters (see Supplementary File 3 for a schematic representation of the ten scenarios tested and Supplementary File 4 for parameters used. We simulated 1 x 10 6 datasets for each explored scenario. To check if the combination of scenarios and prior distributions of their parameters were able to generate datasets similar to the observed one, a Principal Components Analysis (PCA) was performed on the first 10,000 simulated datasets of the reference table in the space of summary statistics. A normalised Euclidean distance between each simulated dataset of the reference table and the observed dataset was calculated to identify the most likely scenario. The range and distribution of priors for parameters used to describe the scenarios (effective population size, time of events, model parameters) are presented in Supplementary File 4. We estimated the type I and type II error according to Cornuet et al. ( 2010 ). Time of differentiation events was calculated considering two generation times a year. Results 1) Population structure The presence of null alleles was negligible (r < 0.05) to moderate (0.05 < r < 0.20) according to the genetic analyses. Two loci (mav6, CM51) were monomorphic for all individuals. Therefore, we used the nine remaining loci to perform the analyses. Clustering simulations with STRUCTURE converged towards the highest posterior probability solution of two distinct genetic clusters (highest ΔK for K = 2), followed by a small peak in ΔK for K = 5 and for K = 9 (Supplementary File 5, A). Discriminant analyses with DAPC showed that the likely number of clusters is between two and ten (Supplementary File 5, B). The FCA grouped the four putative subpopulations (SL) into three different clusters (Supplementary File 5, C, Fig. 2 ). The inconsistencies in the results of population structure analyses were not unexpected given the relatively small number of microsatellite loci used and the uneven sampling across populations (addressed here using FCA and DAPC methods), both of which are known to influence the stability and resolution of clustering algorithms. We therefore interpret the population structure conservatively, acknowledging the limitations while focusing on consistent signals across methods and based on biological knowledge of the species. We recommend treating fine-scale subdivisions with caution and emphasize the need for higher-resolution data (e.g. SNPs, Beez et al. 2024 ) in future studies for more definitive inferences. Under the K = 2 hypothesis (STRUCTURE), we observed a strong division between a first cluster (n = 96) comprising most individuals from SL1 and SL2 (Flanders), and a second cluster (n = 76) with mainly individuals from SL3 and SL4 (Belgium, the Netherlands and Germany) (Fig. 2 ). For K = 3, the individuals from SL3 (Flanders and the Netherlands) and SL4 (the Netherlands, Wallonia and Germany) were further split into their own clusters (hereafter BelNeth and Neth). Under the K = 4 hypothesis, SL3 was split, with the eastern railway verges (hereafter BelNeth_1) separated from the forests (hereafter BelNeth_2) (Fig. 2 ). Under the K = 5 hypothesis, individuals from SL1 were split from SL2 (hereafter Bel_1 and Bel_2 respectively). For K = 6, two clusters arise from SL4 (hereafter Neth_1 and Neth_2) (Fig. 1 , Fig. 2 ). We compared the assignment between STRUCTURE and DAPC analyses for K = 2, K = 3, K = 5, K = 6, and K = 7 (Supplementary File 6). For Kmeans = 2 and Kmeans = 3, the assignment of individuals to clusters in the DAPC analyses were similar to the STRUCTURE results. For Kmeans = 5 and Kmeans = 6, the assignment to the clusters was similar as well, with the exception of the Bel_1 and Bel_2 clusters that showed some differences (Supplementary File 6). Eight and 28 individuals from Bel_1 and Bel_2 respectively formed a first cluster, while the rest of the individuals formed another cluster. For Kmeans = 7, assignment of the clusters was similar except for SL2 that split into three mixed clusters. Regardless of the methods used (STRUCTURE, DAPC, FAC), we observed a strong genetic separation between the populations located in Flanders (Bel), the cross-border population in Flanders and the Netherlands (BelNeth) and the Dutch/Walloon/German population (Neth) (Fig. 2 ). Subsequent analyses were conducted using K = 3 clusters (Bel, BelNeth, Neth). Our dataset showed some limitations when we looked into further subdivisions due to the small number of loci used and the low allelic variability of these markers. Based on our knowledge of the biology of the species, we decided to conduct subsequent analyses with K = 6 clusters (Bel_1, Bel_2, BelNeth_1, BelNeth_2, Neth_1, Neth_2) as defined by STRUCTURE analyses leaving the possibility of other genetic clusters that can only be confirmed by additional markers (e.g. SNPs) and sampling in the area. Contemporary gene flow inferred from BayesAss was lacking between the genetic clusters for K = 3 (Table 1 ). The trend was similar at K = 6, except for Bel_1 where we observed a fraction of 21.4% derived from Bel_2, while there was no migration from Bel_1 to Bel_2. More specifically, migration ancestry for the Bel_1 individual showed that they all have a high probability (> 0.98) of being first generation migrants from Bel_2, while all individuals from Bel_2 have a high probability of being non-migrants. Table 1 BayesAss analysis of migration rates among and within the genetic clusters as identified by STRUCTURE. Means of the posterior distributions of migration rate are shown and represent the fraction of individuals from subpopulation i that are migrants derived from subpopulation j. The rows list the subpopulations from which the individuals were sampled (subpopulations into which individuals migrated). The columns list the subpopulations from which the individuals migrated. Standard deviations for all distributions were < 0.05. Migration from Migration to K = 3 Bel BelNeth Neth Bel 0.9927 0.0037 0.0035 BelNeth 0.0065 0.9865 0.0070 Neth 0.0122 0.0201 0.9670 K = 6 Bel_1 Bel_2 BelNeth_1 BelNeth_2 Neth_1 Neth_2 Bel_1 0.6890 0.2144 0.0222 0.0230 0.0286 0.0227 Bel_2 0.0035 0.9814 0.0036 0.0040 0.0039 0.0036 BelNeth_1 0.0145 0.0139 0.9169 0.0249 0.0140 0.0140 BelNeth_2 0.0088 0.0095 0.0112 0.9489 0.0088 0.0126 Neth_1 0.0155 0.0154 0.0196 0.0180 0.9158 0.0158 Neth_2 0.0222 0.0244 0.0246 0.0236 0.0535 0.8517 2) Genetic diversity Linkage Disequilibrium (LD) was significant for one pair of loci (MAV1 and MAV32) in one subpopulation (Bel_2) (p-adjust < 0.05) (Supplementary File 7). The significance of LD for this pair of loci in this subpopulation might be due to the presence of another genetic cluster that is yet to be confirmed by additional markers. LD can appear when mixing individuals from subpopulations with different allele frequencies. All loci were therefore used for downstream analyses. For K = 3, allelic richness varied from 2.51 (Bel cluster) to 3.41 (Neth cluster) (Table 2 ). HWE significantly deviated for all three genetic clusters, however this is expected for a spatial Wahlund effect. All Fis values fell within the confidence interval (Supplementary File 7), and therefore we tested whether any genetic cluster was more inbred than the two others. Our analyses showed that the inbreeding coefficient estimated for the BelNeth cluster was significantly higher than for the Bel cluster. For K = 6, allelic richness varied from 1.96 to 2.69 (Table 2 ). Not all loci were polymorphic in each genetic cluster. MAV7 was monomorphic in Bel_1, Bel_2, and BelNeth_1. CM40 was monomorphic in Bel_1 and Neth_2. Bel_1 had the lowest genetic diversity with a total number of alleles of 19 and an allelic richness of 1.96, while Neth_2 with the same number of individuals was highly polymorphic with 27 alleles and had the highest allelic richness (2.69). Bel_2 and BelNeth_1 deviated from HWE, but we suspect that this could be due to a spatial Wahlund effect. The Fis values were really high for BelNeth_2 and Bel_1, but still fell within the confidence interval (Supplementary File 8). We therefore tested whether any of the six genetic clusters were more inbred than the others. Pairwise comparisons showed that two subpopulations (BelNeth_2 and Neth_2) had non-overlapping confidence intervals and the inbreeding coefficient estimated for BelNeth_2 was significantly higher than for Neth_2. Table 2 Summary statistics for the nine microsatellite loci among the hazel dormouse subpopulations for K = 3 and K = 6. N: number of individuals, A: number of alleles, Ar: allelic richness, H o : observed heterozygosity, H e : expected heterozygosity, HWE_BH: probability values of concordance with Hardy-Weinberg expectations with corrected p-value (BH), Fis [CI]: global Fis with the lower and upper 95% confidence intervals. N A Ar H o /H e HWE_BH Fis [CI] K = 3 Bel 97 28 2.51 0.32/0.35 0 0.07 [0.004, 0.14] BelNeth 50 32 3.18 0.35/0.48 0 0.25 [0.17,0.33] Neth 25 32 3.41 0.45/0.52 0.005 0.12 [0.006,0.23] K = 6 Bel_1 9 19 1.96 0.23/0.3 0.85 0.24 [-0.004, 0.43] Bel_2 88 27 2.16 0.33/0.34 0.0045 0.02 [-0.04, 0.09] BelNeth_1 18 28 2.59 0.43/0.46 0.03 0.07 [-0.11, 0.22] BelNeth_2 32 28 2.55 0.31/0.39 0.38 0.19 [0.08, 0.29] Neth_1 16 25 2.51 0.46/0.46 0.15 -0.007 [-0.2, 0.13] Neth_2 9 27 2.69 0.42/0.38 1 -0.10 [-0.28, 0.08] The FST and DJost estimates for K = 3 and K = 6 are presented in Table 3 . For K = 3, we observed a high genetic differentiation and low gene flow between the clusters. The highest pairwise value was recorded between clusters Bel and Neth (DJost = 0.20). Genetic differentiation was higher between Bel and BelNeth (0.17) than between BelNeth and Neth (0.14). FST values showed that the gene flow was really low. Highest pairwise values were found between Bel and BelNeth (0.29), and between Bel and Neth (0.30). Gene flow seemed to be lower between Bel and BelNeth (0.29) than between BelNeth and Neth (0.23). For K = 6, highest genetic differentiations (DJost > 0.2) were observed between Bel_1 and BelNeth_1, Bel_1 and Neth_2, Bel_2 and BelNeth_1, Bel_2 and Neth_2, BelNeth_1 and Neth_1, and BelNeth_2 and Neth_2. Genetic differentiation was higher between Bel_2 and BelNeth_1 (DJost = 0.24) than between BelNeth_2 and Neth_1 (0.13). Pairwise estimates of FST showed that gene flow was really low (> 0.2) between all genetic clusters, except for Bel_1 and Bel_2 that had a mean value of 0.14. Bel_1 and Bel_2 also had the lowest genetic differentiation (DJost = 0.02). Table 3 Pairwise DJost (above diagonal) and FST (below diagonal) estimates calculated for K = 3 and K = 6. K = 3 Bel BelNeth Neth Bel / 0.17 [0.14–0.22] 0.20 [0.15–0.28] BelNeth 0.29 [0.27–0.34] / 0.14 [0.11–0.19] Neth 0.30 [0.27–0.36] 0.23 [0.19–0.29] / K = 6 Bel_1 Bel_2 BelNeth_1 BelNeth_2 Neth_1 Neth_2 Bel_1 / 0.02 [0.01–0.06] 0.30 [0.21–0.39] 0.13 [0.08–0.23] 0.09 [0.07–0.17] 0.27 [0.18–0.35] Bel_2 0.14 [0.10–0.26] / 0.24 [0.18–0.30] 0.15 [0.11–0.20] 0.14 [0.09–0.21] 0.37 [0.29–0.44] BelNeth_1 0.34 [0.31–0.45] 0.37 [0.34–0.42] / 0.09 [0.06–0.16] 0.25 [0.19–0.35] 0.18 [0.12–0.30] BelNeth_2 0.33 [0.29–0.44] 0.34 [0.32–0.39] 0.23 [0.20–0.31] / 0.13 [0.09–0.21] 0.22 [0.18–0.29] Neth_1 0.25 [0.21–0.38] 0.32 [0.28–0.40] 0.30 [0.27–0.40] 0.32 [0.27–0.41] / 0.12 [0.08–0.23] Neth_2 0.39 [0.35–0.53] 0.45 [0.40–0.52] 0.28 [0.24–0.38] 0.38 [0.34–0.47] 0.27 [0.24–0.38] / 3) Divergence time Our DIYABC analysis provides a preliminary estimate of divergence time, as we acknowledge the limited power of our dataset. To mitigate over-interpretation, we focused on the relative timing of divergence events and their biological plausibility, rather than precise dating. Future studies incorporating genome-wide SNP data would provide more robust estimates which are beyond the scope of this paper. Analysis with DIYBAC identified scenario 10 as the most probable, with moderately strong posterior probability (0.5376) (Supplementary File 9, A). This scenario suggests that two parental subpopulations with constant effective size diverged around 272 generations ago [CI: 57–758] (i.e. 136 years ago [28–379]) and got admixed around 122 generations ago [CI: 8-350] (i.e. 61 years ago [4-175]), giving birth to a third subpopulation. The representation of demographic scenario 10 and parameter estimates (Ne: effective population size [confidence interval], T: generations, ra: admixture rate, Na: ancestral population of size NA) for hazel dormouse subpopulations for K = 3 (Bel, BelNeth, Neth) is available in Supplementary File 10. Population size was estimated at N = 254 [65–760], N = 355 [63–914], and N = 620 [188–974] for N1 (Bel), N2 (BelNeth), and N3 (Neth) respectively. The pre-evaluation scenario prior combination showed that the model (scenario and parameter prior definition) was not off the target (Supplementary File 9, B). Model checking analyses showed as well that the chosen model/posterior correctly explains the observed dataset (Supplementary File 9, C). The type I error (probability that scenario 10 is rejected although true) was 0.215, while the type II error (probability of choosing scenario 10 when it is not the true scenario) was 0.293 (293 counting decisions over 1,000 in favour of scenario 10). Although scenario 10 had a low type II error, this scenario is likely to be the most probable of all proposed scenarios. Discussion 1) Genetic differentiation as a result of past human activities Our results suggest that the landscape management practices from the last century had an impact on the current genetic structure of the hazel dormouse population in the Meuse-Rhine Euregion. The hazel dormouse is an arboreal rodent that is strongly associated with deciduous or mixed deciduous-coniferous forests that have a well-developed understory (Juškaitis 2008 ; Juškaitis and Büchner 2013 ; Goodwin et al. 2018 ). The interaction between cultural development and the natural environment has influenced the European forest composition and structure since the last ice age (Brewer et al. 2002 ; Bradshaw 2004 ; Berglund et al. 2008 ; Overballe-Petersen et al. 2014 ). In Northwest Europe, the Holocene is characterised by different periods of long-term deforestation/regeneration patterns (Berglund 2003 ). Demographic pressure due to the establishment of cities and intensive agriculture due to a climatic amelioration led to woodland clearance to create more farmlands (Berglund 2003 ; Kaplan et al. 2009 ). In Europe, forest areas reached an all-time low around 1850 (e.g. only 2% of the original forests remained in the Netherlands, Mohren and Vodde 2006 ) after which European countries experienced forest transitions (net reforestation) (Kaplan et al. 2012 ; McGrath et al. 2015 ). These events have probably acted as a controlling factor on the distribution of arboreal species such as the hazel dormouse. We estimated a first separation between the Bel (SL1/SL2) and the Neth clusters (SL4) around 136 years ago, admixing into the third subpopulation BelNeth (SL3) around 61 years ago. A comparison between the present situation and a detailed historical map from 1867 (Kadaster 2025 ) does not show major changes in size and connectivity of the forests at first sight (Fig. 3 ). However, the presence of more stepping stones, better developed hedges and hedgerows, a wider distribution and probably higher densities of hazel dormice due to higher habitat quality, may all have contributed to a higher exchange rate of individuals between the forests in the past compared to now. Forests were somewhat larger in 1867, with more forest patches connecting BelNeth_2 and Neth_1. In 1867, the forests within Bel_2 (SL2) were larger and better connected, forming a broad forest belt. Bel_2 (SL2) was larger on the east side. This part of the forest gradually disappeared, temporarily increasing the distance between Bel_2 (SL2) and BelNeth_1 (SL3). The construction of the railway during the First World War led to the development of the eastern railway verges that now form a large part of the connection between Bel_2 and BelNeth_1. Hazel dormice were also previously present in the forests west-northwest of Bel_2, some of which remain connected to Bel_2 but have experienced such severe habitat degradation that they can no longer support a hazel dormouse subpopulation. So there may have been a more important connection between Bel_2 and BelNeth in the past, largely formed by these forests and the green belt between them and BelNeth_2 shown on Fig. 3 . From the last quarter of the 19th century, the traditional hedge landscapes in Western Europe began to deteriorate, but it was only in the period 1950–1975 that the disappearance of mixed woodlands and pastures reached its peak (Van Driessche 2019 ). This decline was due to three anthropological factors: 1) introduction of barbed wire, 2) declining demand for firewood and convenience wood and 3) increase in scale and mechanisation of agriculture. 2) Genetic structure and diversity The present study shows a significant genetic differentiation within the hazel dormouse population in the Meuse-Rhine Euregion, dividing it into spatially isolated subpopulations inhabiting poorly connected forest fragments, with low genetic exchange. This is further demonstrated by the virtually non-existent migration rate between the different patches. Similar results with strong genetic differentiation among populations have already been observed in hazel dormouse populations in Central Italy (Bani et al. 2017 ), but also for several other arboreal mammals, such as edible dormouse, squirrel glider, and ringtail possum (Taylor et al. 2011 ; Fietz et al. 2014 ; Lino et al. 2019 ). This study demonstrates low allelic richness in all surveyed hazel dormouse subpopulations (Ar from 1.96 to 2.69). Our results thus mirror previous studies that examined the impact of forest isolation on genetic structure of hazel dormouse populations, showing a significant genetic isolation of population fragments (Mills 2012 ; Naim et al. 2012 ; Bani et al. 2017 , 2018 ; Friebe et al. 2018 ). A study on the effect of forest fragmentation on hazel dormice in Italy showed that the physical link between woodlots was the most important parameter to avoid isolation (Capizzi et al. 2002 ). Not only between, but also within forests connectivity will improve sustainability of hazel dormouse populations. Mortelliti et al. ( 2013 ) observed that in isolated habitat patches, linked trees and a scrub layer are important for successful hazel dormouse conservation. Another study in central Italy revealed that higher abundance of shrubs favoured higher abundance of individuals, while a higher diversity of resources had a direct positive effect on survival (Sozio et al. 2016 ). The authors showed that regrowing forest represents the most suitable habitat for hazel dormice, while coppice (< five years regrowing) was unsuitable although a study showed that hazel dormice used coppice again after 2–3 years on the railway verges in Belgium (Verbeylen, pers.com). Goodwin et al. ( 2018 ) and Juškaitis ( 2020 ) observed the same pattern for the UK and Lithuania respectively. All these studies showed that active management of woodland providing sufficient early successional habitats and other dense vegetation will benefit the species’ conservation while it is still compatible with timber production. In addition to anthropogenic impact of forest management on arboreal species, studies showed that dispersal of hazel dormice is influenced by the presence of barriers such as roads or discontinuous hedgerows (Naim et al. 2012 ; Bani et al. 2018 ; Friebe et al. 2018 ). However, Combe ( 2018 ) demonstrated that the roads do not disrupt the dispersal but rather the width of the road and the continuity of the roadside habitat. Even barriers such as 30 m wide motorways can be crossed repeatedly though (Kelm et al. 2015 ), and roadside shrubs are suitable habitats for reproduction (Friebe et al. 2018 ). Bani et al. ( 2018 ) stressed the importance of improving the continuity and quality of hedgerows (e.g. by developing a dense and diversified shrub layer), as discontinuous hedgerows are unsuitable for hazel dormice and could represent an ecological trap for the species. Our study suggests that railway verges with scrubs can represent valuable connections between fragmented habitats. Where hazel dormice inhabit the Flemish railway verges, the typical management scheme was slightly adapted for their benefit, with the 4 m wide inner zone of dense scrub mowed in winter instead of summer, the 4 m wide central zone of diverse shrub species coppiced more small-scale, and the higher trees in the outer zone were only cut if necessary for safety reasons. This results in the continuous presence of all essential habitat elements within a narrow strip, provided the management scheme is adhered to and no additional detrimental maintenance practices are implemented. Consequently, the western railway verges—part of the Bel_2 cluster—harbor particularly high densities of hazel dormice and likely function as a source population for the surrounding forests. This may also account for the observed dispersal rates from Bel_2 to Bel_1 (see BayesAss analysis), despite the poor connectivity between these two clusters. A few individuals originating from BelNeth_1 were geographically located within Bel_2, and one disperser from Bel_2 was found in BelNeth_2 (Fig. 1 ), suggesting that the high-quality habitat along the railway corridor may facilitate colonization of new areas. Field data indicate that minor interruptions in woody vegetation do not constitute significant barriers for hazel dormice, as individuals have been observed to cross open spaces over distances of up to 500 m (Büchner 2008 ; Lemmers et al. 2022 ). Whether they do this or not, will also depend on how much pressure they suffer (e.g. if all space in their patch of birth has been taken, they are forced to settle elsewhere). And not only during dispersal, but also during daily home range use, roads, railways and railway bridges can be crossed often (Verbeylen et al. 2015 , 2017 ; Lemmers et al. 2022 ). 3) Conservation advice Hazel dormouse densities in BelNeth_1 are much lower than in Bel_2 based on the number of shrub nests found during the annual monitoring and the amount of suitable edges and other high-quality habitat in the form of dense thicket (Verbeylen et al. 2020 ). This could explain the relatively large inbreeding coefficient for BelNeth_1 (Table 2 ). The population size estimated with DIYABC for Bel and BelNeth is quite similar, while the population size for Neth is larger. This is likely due to the connection to the adjacent German and Walloon forests. While Neth_1 and Neth_2 do not seem to suffer from inbreeding depression and have a decent population size, maintaining a heterogeneous habitat and active woodland management is still essential for the survival of the species in this cross-border region. The highest priority is to increase hazel dormouse densities in BelNeth (through habitat improvement) and to connect it to the surrounding populations Bel_2 and Neth_1. The importance of the connection between BelNeth and Bel_2 was already stressed in 2013 (Verbeylen et al. 2013 ) and in 2017, it was incorporated in the Flemish hazel dormouse species protection plan (Nijs and Verbeylen 2017 ) and is now being put into practice. Further, we strongly suggest the use of higher-resolution data such as the SNP panel (Beez et al. 2024 ) for future genetic monitoring of the species. In conclusion, our study indicated low genetic diversity associated with low gene flow between subsequent hazel dormouse subpopulations in the Meuse-Rhine Euregion, which might impede adaptive responses to any future deterioration of their natural environments. From a conservation point of view, this includes the development and maintenance of corridors in the matrix between woodlands and small habitats in the landscape as well as corridors between habitats inside forests. This will restore habitat connectivity, improve forest habitats and prevent further genetic erosion for the hazel dormouse population in the cross-border region. The current action plans for Flanders (Nijs and Verbeylen 2017 ) and the Netherlands (Lemmers et al. 2019 ), aiming to further improve and restore habitat connections, are being implemented and will make the hazel dormouse metapopulation more robust. We expect that ecologically functioning corridors between the hazel dormouse subpopulations in the Meuse-Rhine Euroregion will significantly decrease inbreeding risks within each of these subpopulations, favouring their long-term survival. Declarations Ethics approval Sampling was licensed by local or regional authorities and samples were collected under permission of the Dutch Fauna and Flora Act (license number FF/75A/2012/037 of the Dutch Mammal Society) and the Decree of the Flemish Government on species protection and species management (license number ANB/BL-FF/V11-00123 of Natuurpunt Studie). Consent to publish All authors approved the version to be published. Funding This work was supported by a research credit of the Fonds de la Recherche Scientifique – FNRS to Johan Michaux and Alice Mouton was a FRIA grantee (Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture) of the Fonds de la Recherche Scientifique – FNRS at the time of research. The field work was partially supported by a biodiversity project of the Flemish Province of Limburg, and the Agency for Nature and Forests of the Flemish Government (ANB), the Research Institute for Nature and Forest (INBO), Natuurpunt Studie (Research Department of Natuurpunt), Dienst Landelijk Gebied (DLG) as part of the Dutch Ministry of Economic Affairs and by Interreg project ‘Interreg IV Maas-Rijn Habitat Euregio’ (subproject ‘recovery plan hazel dormouse’). Author Contribution AM and JM conceived the project and designed the research. GV, RK coordinated the sample collection efforts. GV, RK provided and/or collected samples. AM conducted the laboratory work, the analyses and interpretation of the genetic data. PL prepared figure 1,3. AM wrote the manuscript with input from all coauthors, particularly for the Sampling and Study Area (Materials and Methods) and Discussion sections. All coauthors reviewed and commented the manuscript. All authors approved the final manuscript. Acknowledgement In Flanders, sample collection by the Mammal Working Group of Natuurpunt was mainly volunteer work. For the Netherlands, Wallonia and Germany, samples were collected during the Interreg project ‘Interreg IV Maas-Rijn Habitat Euregio’ (subproject ‘recovery plan hazel dormouse’) by the Dutch Mammal Society, Natuurbalans – Limes Divergens and Natuurpunt Studie. We would like to thank the following people for assisting and enabling us to conduct this study: Martijn Dorenbosch, Gerald Driessens, Ruud Foppen, Griet Nijs, Rik Palmans, Rian Pulles, Jip Ramakers, Rick Reijerse, Sander van de Koppel, Wim van Mourik, Ivo Vanseuningen, Dominique Verbelen and Ludy Verheggen. ANB, INFRABEL, Limburgs Landschap, Natuurmonumenten, Staatsbosbeheer, Stichting ARK and several municipalities and private landowners granted permission to access the study sites. Data Availability Data is provided within the supplementary information files. 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(in Dutch) Wilson GA, Rannala B (2003) Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163:1177–1191 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFileEJWR.zip Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6545691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525509407,"identity":"b5ff3197-e03f-49a7-b339-76f6775c50f5","order_by":0,"name":"Alice Mouton","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBADOQZ2xgYQI4E49QcYGIwZmEnVktjADGET1sI/7fAx6Q8Vd9I3HGZufPilgiGPn5AWidtpaRIHzjzL3XCYsdlY5gxDsWQDIT23c8wkDrYdBmlpk5ZsY0jccICADnmolnSDw4ztvyX/MSTuJ6TFAKolAailjfFjA9AWQu4yvJ2WbHHmzGHDmUC/SDMckyiWIGSL3O3kgzcqKg7L8x1vf/jxR41NHn8DIWuQATMPgwQp6oGA8QeJGkbBKBgFo2BkAADWrUY5bbXfjAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Liege","correspondingAuthor":true,"prefix":"","firstName":"Alice","middleName":"","lastName":"Mouton","suffix":""},{"id":525509408,"identity":"c7b2e24e-02a8-42bb-9271-6b1a529a79f9","order_by":1,"name":"Goedele Verbeylen","email":"","orcid":"","institution":"Natuurpunt Studie / Mammal Working Group","correspondingAuthor":false,"prefix":"","firstName":"Goedele","middleName":"","lastName":"Verbeylen","suffix":""},{"id":525509409,"identity":"31765b69-0a9a-4529-817d-15516f6c3a15","order_by":2,"name":"Pim Lemmers","email":"","orcid":"","institution":"Natuurbalans – Limes Divergens BV","correspondingAuthor":false,"prefix":"","firstName":"Pim","middleName":"","lastName":"Lemmers","suffix":""},{"id":525509410,"identity":"839363c9-e5ba-484e-9c0f-9e55c9987e33","order_by":3,"name":"Maurice La Haye","email":"","orcid":"","institution":"Radboud University","correspondingAuthor":false,"prefix":"","firstName":"Maurice","middleName":"La","lastName":"Haye","suffix":""},{"id":525509411,"identity":"53cac9a6-c2f6-47b8-adf4-91230a8ef47b","order_by":4,"name":"René Krekels","email":"","orcid":"","institution":"Natuurbalans – Limes Divergens BV","correspondingAuthor":false,"prefix":"","firstName":"René","middleName":"","lastName":"Krekels","suffix":""},{"id":525509413,"identity":"e075df18-de5b-43ef-bd82-f8fa64f0b08f","order_by":5,"name":"Johan Michaux","email":"","orcid":"","institution":"University of Liege","correspondingAuthor":false,"prefix":"","firstName":"Johan","middleName":"","lastName":"Michaux","suffix":""}],"badges":[],"createdAt":"2025-04-28 08:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6545691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6545691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93823725,"identity":"a43ca750-6c8a-43b0-8082-9466f3d803cf","added_by":"auto","created_at":"2025-10-18 05:33:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7667271,"visible":true,"origin":"","legend":"","description":"","filename":"EuropeanJournalWildliferesearchSubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/6742993f61a201a9cd22afa8.docx"},{"id":93823716,"identity":"35f45ee7-ec11-41b0-aad0-7ef75c56b3ac","added_by":"auto","created_at":"2025-10-18 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05:33:28","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169875,"visible":true,"origin":"","legend":"","description":"","filename":"553d0dbaeb4c45998fe53d72382481b81structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/04cd451df0d6bc630eb27616.xml"},{"id":93824250,"identity":"a81da6ae-ee43-4eea-aa19-a2fbba6f90a6","added_by":"auto","created_at":"2025-10-18 05:41:28","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177479,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/c2be6ceadbbd0786a2f1404e.html"},{"id":93823717,"identity":"95fd101e-f4c3-4527-9392-711df6edfc61","added_by":"auto","created_at":"2025-10-18 05:33:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193849,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of the sampled hazel dormice in the Meuse-Rhine Euregion, grouped into four Sampling Locations (SL1 - SL4). The pie charts show for each individual the estimated membership fractions of the six inferred genetic clusters (STRUCTURE).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/1e0c38de887b7abbd0998a15.png"},{"id":93824245,"identity":"b53389ed-ea97-4246-a065-639b7a8c4620","added_by":"auto","created_at":"2025-10-18 05:41:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34428,"visible":true,"origin":"","legend":"\u003cp\u003eA) Estimated population structure from STRUCTURE analyses for K = 2 to K = 6. Each individual is represented by a thin vertical line divided into K coloured segments that represent the individual’s estimated membership fractions in K clusters. Samples are organised by original sampling locations. B) Discriminant Analysis of Principal Components plot showing the position of six clusters (determined by k-means algorithm) of hazel dormice. Colours correspond to similar clusters determined by STRUCTURE; C) Factorial correspondence analysis showing relationships among the genotypes of the four on sampling location. Coloured points represent the individual genotype for each sample. SL = Sampling Locations.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/da6d657b53397ec85dfc33e7.png"},{"id":93824330,"identity":"df64b513-f847-40a3-b688-c3ddca64609f","added_by":"auto","created_at":"2025-10-18 05:49:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":645444,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons between the forest cover in the study area in 1867 and in 2018. The brown colour depicts overlap between 1867 and 2018. The background map originates from 1867 (Kadaster 2025). Sampling locations are indicated with SL. The land border has not changed between both periods.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/88ba21aab322d0af060278b1.png"},{"id":104577205,"identity":"d64546a8-f121-46ea-807f-91fa689dd912","added_by":"auto","created_at":"2026-03-13 14:11:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1941057,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/85163bc7-5b6e-442b-8ece-5bbb5e6491b4.pdf"},{"id":93823720,"identity":"615b530d-529d-42a5-9edf-a48e1d1f62ce","added_by":"auto","created_at":"2025-10-18 05:33:27","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":940419,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileEJWR.zip","url":"https://assets-eu.researchsquare.com/files/rs-6545691/v1/df18ec373bf4cdfd8a274d1c.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population-genetic structure of the hazel dormouse Muscardinus avellanarius in the Meuse-Rhine Euregion","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpatial organisation of wildlife populations and their genetic diversity is impacted by habitat loss and fragmentation (Coster and Kovach \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kuemmerle et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hindrikson et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mona et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). With reduced movement or gene flow between habitat patches, fragmentation can effectively reduce population sizes, thereby increasing genetic drift and the risk of inbreeding depression, especially in arboreal species (Bijlsma and Loeschcke \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fietz et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Friebe et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Population structure and patterns of genetic diversity might also be influenced by past anthropogenic activities. Alteration of the landscape is a process that started since the establishment of the first agricultural societies during the Stone Ages in the mid-Holocene and then grew larger during the subsequent Bronze, Iron, Roman and Middle Ages (Ruddiman \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kaplan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Changes in agricultural practice have influenced forest composition and structure through deforestation, slash-and-burn agriculture and clearing (Eriksson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bradshaw \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). It can take many generations for changes in level of gene flow or genetic drift to be translated into detectable effects on genetic structure in mammals (Keyghobadi \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The influence of human civilisation on geographical distribution and population density of European arboreal species such as edible dormouse (\u003cem\u003eGlis glis\u003c/em\u003e) and hazel dormouse (\u003cem\u003eMuscardinus avellanarius\u003c/em\u003e) was suggested by Carpaneto and Cristaldi (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) through a historical and biogeographical analysis, supported by paleontological data. A genetic study on the edible dormouse across its distribution supported this hypothesis and showed that the current structure of this species is a result of past anthropogenic forest fragmentations that occurred from the Holocene to the Middle Ages (Michaux et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this context, we presume that the hazel dormouse might be an appropriate study species for testing whether an effect of past anthropogenic activities on genetic structure is present in the Meuse-Rhine Euregion. The hazel dormouse is a glirid species typical of forested habitats, thriving best in the densest parts \u0026ndash; such as well-developed forest edges and canopies, hedgerows and early successional shrub habitats \u0026ndash; with a high diversity in seed-and-fruit-bearing plant species (Ehlers \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Juškaitis and B\u0026uuml;chner \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Goodwin et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hazel dormice usually overwinter solitary and on forest floors, not far from the forest edge (Verbeylen et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gubert et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides providing valuable habitat, dense, varied hedgerows and hedges also act as corridors for dispersal. Radio-tracking carried out in the UK indicated that hazel dormice were almost completely arboreal and reluctant to cross open ground during their normal nocturnal activity (Bright and Morris \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). However, other studies showed that they can cross open fields over a distance of up to 500 m (B\u0026uuml;chner \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mortelliti et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lemmers et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and up to 30 m wide roads (Chanin and Gubert \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Verbeylen et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lemmers et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and even have home ranges spanning both sides of railways, railway bridges and railway service roads (Verbeylen et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVulnerable to habitat loss and disruption of the structural connectivity provided by hedgerows (Capizzi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mortelliti et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the hazel dormouse is listed under the Annex IV of the European Union Habitats Directive and Annex III of the Bern Convention. Genetic studies on hazel dormouse populations have been conducted in the UK, Ireland, Italy, and Germany (Mills \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Naim et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Glass et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bani et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Friebe et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These studies demonstrated that habitat fragmentation negatively impacts the genetic structure of the species and therefore the long-term survival of populations. Conservation plans (e.g. conservation and improvement of habitat quality, amount and connectivity, reintroduction, breeding programmes) have been prepared and are being implemented in the north-western part of the species range where populations have decreased dramatically (Foppen et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kuijsten and Krekels \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Schulz et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nijs and Verbeylen \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dietz et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lemmers et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Verbeylen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bullion et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In order to gain more understanding about the impact of habitat fragmentation on genetic diversity and population structure of the hazel dormouse in the Meuse-Rhine Euregion (located at the tripoint of Belgium, the Netherlands and Germany), this study was conducted with the following aims: (1) to assess and describe the genetic diversity and population structure in the cross-border hazel dormouse population using microsatellite markers, and (2) to discuss the implications of these results for the development of a sustainable cross-border hazel dormouse population.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1) Study area\u003c/h2\u003e\u003cp\u003eIn Flanders (Belgium), the hazel dormouse used to be distributed in most provinces (West Flanders, East Flanders, Flemish Brabant and Limburg), but strongly declined from the second half of the 20th century. The whole Flemish population seems to be restricted now to the eastern part of the municipality of Voeren (province of Limburg) where the forests inhabited by the hazel dormouse stretch into the Netherlands, Wallonia (Belgium) and Germany (Verbeylen et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Wallonia, the hazel dormouse is found mainly south of the rivers Sambre and Meuse with few observations in the north of the region (Schockert et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the Netherlands, the species is only found in the southernmost part of the country. It used to be present in a larger part of the province of Limburg, but the distribution declined from the second half of the 20th century. The occurrence is currently limited to the south-easternmost part of the province, but numbers are increasing (Lemmers et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the Meuse-Rhine Euregion, the species\u0026rsquo; presence was confirmed in a series of Walloon and German forests adjacent to the Flemish and Dutch hazel dormouse areas (Verbeylen and Kuijsten \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2) Sampling\u003c/h3\u003e\n\u003cp\u003eFrom 2004 to 2011, a total of 232 hazel dormouse DNA samples were collected in the Meuse-Rhine Euregion. Small tufts of hairs were collected from animals trapped in nest boxes and nest tubes using tweezers and stored immediately in ethanol. Individuals were grouped into four putative subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The forest names and sampling locations in the study area are available in Supplementary File 1. Sampling location 1 (hereafter SL1, n\u0026thinsp;=\u0026thinsp;11) comprises individuals from Vrouwenbos-Stroevenbos-Sint-Gillisbos (Voeren, Flemish Limburg, Belgium), sampling location 2 (SL2, n\u0026thinsp;=\u0026thinsp;124) was located in Broekbos, Konenbos, Veursbos-Roodbos-Vossenaerde and the western railway verges (Voeren, Flemish Limburg, Belgium), sampling location 3 (SL3, n\u0026thinsp;=\u0026thinsp;63) groups individuals from Teuvenerberg-Gulpdal-Obsinnich and the eastern railway verges (Voeren, Flemish Limburg, Belgium) and from Bovenste Bosch, Kruisbosch, Groote Bosch and Schweibergerbosch (Dutch Limburg, the Netherlands). Individuals from sampling location 4 (SL4, n\u0026thinsp;=\u0026thinsp;32) originated from Vijlenerbossen, Schimpersbos and Preusbos (Dutch Limburg, the Netherlands) and Preuswald and Aachenerwald (Germany, and Wallonia, Belgium). The samples from Voeren (Flanders, Belgium, n\u0026thinsp;=\u0026thinsp;186) were collected in the frame of a CMR-study by the Mammal Working Group of Natuurpunt Studie. The remaining 46 samples were collected during the Interreg project \u0026lsquo;Interreg IV Maas-Rijn Habitat Euregio\u0026rsquo; (subproject \u0026lsquo;recovery plan hazel dormouse\u0026rsquo;). The Dutch Mammal Society and Natuurbalans collected samples from 37 individuals present in nest tubes in the Netherlands. Natuurpunt Studie collected 9 samples from individuals occupying shrub nests (5 in Wallonia, Belgium, 3 in Germany and 1 in the Netherlands). DNA extractions with controls were carried out using a QIAmp DNA Micro kit (Qiagen) following manufacturer's instructions. All samples were handled using sterile disposable materials.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3) Microsatellite genotyping\u003c/h3\u003e\n\u003cp\u003eEleven successfully amplified loci (five after Naim et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e: mavE3, mavB5, mavG3, mavG6, mavA5, and six from Mills et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e: Mav03, Mav021, Mav032, Mav036, Mav051, Mav040) were combined into multiplex sets according to their size and fluorescent label and subsequently amplified via multiplex polymerase chain reactions (PCR) in a Mastercycler ep Gradient (Eppendorf). The multiplex PCR samples contained 1 \u0026micro;l DNA, 5 \u0026micro;l of Multiplex PCR MasterMix (Qiagen), 0.2 \u0026micro;M of each primer and deionised water to the final volume of 10 \u0026micro;l. Cycling conditions included an initial step at 95\u0026deg;C for 15 min, followed by 35 cycles of denaturation at 94\u0026deg;C for 30 s, annealing at 60\u0026deg;C for 90 s, and extension at 72\u0026deg;C for 30 min. 2 \u0026micro;l of PCR product was mixed with 0.3 \u0026micro;l of Liz GS500 dye (Applied Biosystems) and 12 \u0026micro;l of Hi-Di formamide and loaded onto an ABI 3130 Genetic Analyzer at the University of Brussels. The yielded DNA fragments were analysed using GeneMapper v.4.1 software (Applied Biosystems). Data was converted into STRUCTURE and Genepop format using Convert (Glaubitz \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), into Fstat DAT format using Genetix (Belkhir \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and into Immanc for BA3 using Formatomatic (Manoukis \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Around 25 percent of the samples were collected twice, and the amplification was therefore carried out multiple times on a subset of samples.\u003c/p\u003e\u003cp\u003e4) Population structure\u003c/p\u003e\u003cp\u003eOnly one sample was used per individual and samples with more than ten percent of missing data were removed, leading to a final dataset of 172 samples (SL1: n\u0026thinsp;=\u0026thinsp;9, SL2: n\u0026thinsp;=\u0026thinsp;88, SL3: n\u0026thinsp;=\u0026thinsp;50, SL4: n\u0026thinsp;=\u0026thinsp;25). Data with sample name, country, sampling location and genotypes are available in Supplementary File 2. The frequency of null alleles was estimated per locus and per sampling location using the expectation maximum algorithm (EMA) implemented in FreeNA with 1000 replications (Chapuis and Estoup \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe used several approaches to estimate the population structure so that an informative K could be chosen (Janes et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). First, we used a Bayesian assignment as implemented in STRUCTURE v.2.4 (Pritchard et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Ten iterations were run for each value of K from one to ten using an admixture model, correlated allele frequencies and with a burn-in of 1 x 10\u003csup\u003e5\u003c/sup\u003e and Markov chain Monte Carlo (MCMC) values of 1 x 10\u003csup\u003e6\u003c/sup\u003e. LOCPRIOR models were used with the original sample location to incorporate sampling location without forcing structure. The results were analysed with STRUCTURE Harvester (Earl and von Holdt \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to identify the optimal number of clusters. A visual output of the STRUCTURE results was generated using Clumpack (Kopelman et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Then we used a Discriminant Analysis of Principal Components (DAPC) and a Factorial correspondence analysis (FCA) implemented in R v.3.6 (R Core Team \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with the adegenet package (Jombart et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and in GENETIX v.4.05.2 (Belkhir \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) respectively as these methods do not make prior assumptions about the population structure model (e.g. HW and gametic equilibrium not assumed) and work well with sample unevenness. We used the function \u0026lsquo;find.clusters\u0026rsquo; to identify clusters and then \u0026lsquo;dapc\u0026rsquo; to describe the relationships between the clusters using the sampling locations as prior information. DAPC was run using the sampling locations as prior information and then using the genetic structure from the several selected K in STRUCTURE.\u003c/p\u003e\u003cp\u003eTo estimate contemporary gene flow (within the past one to three generations) among the genetic clusters, we used the Bayesian approach as implemented in the programme BA3 v.3.0.4 (Wilson and Rannala \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). As recommended in the user manual, mixing parameters (allele frequencies (a), inbreeding (f), and migration rate (m)) were all varied until acceptance rates were ~\u0026thinsp;30\u0026ndash;40%. Once mixing rates were optimised (a\u0026thinsp;=\u0026thinsp;0.3, f\u0026thinsp;=\u0026thinsp;0.4 for K\u0026thinsp;=\u0026thinsp;3), five independent runs with varying random seed numbers (100, 10 (default), 4,656, 246, 25) were performed using 1 x 10\u003csup\u003e7\u003c/sup\u003e iterations, a burn-in of 1 x 10\u003csup\u003e6\u003c/sup\u003e and a sampling frequency of 1,000. The programme TRACER v.1.7.1 (Rambaut et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used as a method to qualitatively assess MCMC convergence.\u003c/p\u003e\n\u003ch3\u003e5) Genetic diversity\u003c/h3\u003e\n\u003cp\u003eLinkage disequilibrium (LD) was calculated in FSTAT v.2.9.3.2 (Goudet \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) with 10,000 permutations. We used the function \u0026lsquo;divBasic\u0026rsquo; and \u0026lsquo;fastDivPart\u0026rsquo; in the diveRsity package (Keenan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in R v.3.6 (R Core Team \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to calculate genetic diversity indices such as allelic richness (ar), expected (He) and observed (Ho) heterozygosity, and F-statistics (pairwise FST, DJost, Gst and Fis).\u003c/p\u003e\n\u003ch3\u003e6) Population divergence and history\u003c/h3\u003e\n\u003cp\u003eWe tested ten different demographic scenarios using the ABC framework implemented in DIYABC v.2.1 (Cornuet et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We tested whether the main identified clusters originated from the fragmentation of an ancestral panmictic population or if one of them originated from an isolated subpopulation, and whether admixture events occurred between clusters (see Supplementary File 3 for a schematic representation of the ten scenarios tested and Supplementary File 4 for parameters used. We simulated 1 x 10\u003csup\u003e6\u003c/sup\u003e datasets for each explored scenario. To check if the combination of scenarios and prior distributions of their parameters were able to generate datasets similar to the observed one, a Principal Components Analysis (PCA) was performed on the first 10,000 simulated datasets of the reference table in the space of summary statistics. A normalised Euclidean distance between each simulated dataset of the reference table and the observed dataset was calculated to identify the most likely scenario. The range and distribution of priors for parameters used to describe the scenarios (effective population size, time of events, model parameters) are presented in Supplementary File 4. We estimated the type I and type II error according to Cornuet et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Time of differentiation events was calculated considering two generation times a year.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e1) Population structure\u003c/h2\u003e\u003cp\u003eThe presence of null alleles was negligible (r\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to moderate (0.05\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.20) according to the genetic analyses. Two loci (mav6, CM51) were monomorphic for all individuals. Therefore, we used the nine remaining loci to perform the analyses.\u003c/p\u003e\u003cp\u003eClustering simulations with STRUCTURE converged towards the highest posterior probability solution of two distinct genetic clusters (highest ΔK for K\u0026thinsp;=\u0026thinsp;2), followed by a small peak in ΔK for K\u0026thinsp;=\u0026thinsp;5 and for K\u0026thinsp;=\u0026thinsp;9 (Supplementary File 5, A). Discriminant analyses with DAPC showed that the likely number of clusters is between two and ten (Supplementary File 5, B). The FCA grouped the four putative subpopulations (SL) into three different clusters (Supplementary File 5, C, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe inconsistencies in the results of population structure analyses were not unexpected given the relatively small number of microsatellite loci used and the uneven sampling across populations (addressed here using FCA and DAPC methods), both of which are known to influence the stability and resolution of clustering algorithms. We therefore interpret the population structure conservatively, acknowledging the limitations while focusing on consistent signals across methods and based on biological knowledge of the species. We recommend treating fine-scale subdivisions with caution and emphasize the need for higher-resolution data (e.g. SNPs, Beez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in future studies for more definitive inferences.\u003c/p\u003e\u003cp\u003eUnder the K\u0026thinsp;=\u0026thinsp;2 hypothesis (STRUCTURE), we observed a strong division between a first cluster (n\u0026thinsp;=\u0026thinsp;96) comprising most individuals from SL1 and SL2 (Flanders), and a second cluster (n\u0026thinsp;=\u0026thinsp;76) with mainly individuals from SL3 and SL4 (Belgium, the Netherlands and Germany) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For K\u0026thinsp;=\u0026thinsp;3, the individuals from SL3 (Flanders and the Netherlands) and SL4 (the Netherlands, Wallonia and Germany) were further split into their own clusters (hereafter BelNeth and Neth). Under the K\u0026thinsp;=\u0026thinsp;4 hypothesis, SL3 was split, with the eastern railway verges (hereafter BelNeth_1) separated from the forests (hereafter BelNeth_2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Under the K\u0026thinsp;=\u0026thinsp;5 hypothesis, individuals from SL1 were split from SL2 (hereafter Bel_1 and Bel_2 respectively). For K\u0026thinsp;=\u0026thinsp;6, two clusters arise from SL4 (hereafter Neth_1 and Neth_2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe compared the assignment between STRUCTURE and DAPC analyses for K\u0026thinsp;=\u0026thinsp;2, K\u0026thinsp;=\u0026thinsp;3, K\u0026thinsp;=\u0026thinsp;5, K\u0026thinsp;=\u0026thinsp;6, and K\u0026thinsp;=\u0026thinsp;7 (Supplementary File 6). For Kmeans\u0026thinsp;=\u0026thinsp;2 and Kmeans\u0026thinsp;=\u0026thinsp;3, the assignment of individuals to clusters in the DAPC analyses were similar to the STRUCTURE results. For Kmeans\u0026thinsp;=\u0026thinsp;5 and Kmeans\u0026thinsp;=\u0026thinsp;6, the assignment to the clusters was similar as well, with the exception of the Bel_1 and Bel_2 clusters that showed some differences (Supplementary File 6). Eight and 28 individuals from Bel_1 and Bel_2 respectively formed a first cluster, while the rest of the individuals formed another cluster. For Kmeans\u0026thinsp;=\u0026thinsp;7, assignment of the clusters was similar except for SL2 that split into three mixed clusters.\u003c/p\u003e\u003cp\u003eRegardless of the methods used (STRUCTURE, DAPC, FAC), we observed a strong genetic separation between the populations located in Flanders (Bel), the cross-border population in Flanders and the Netherlands (BelNeth) and the Dutch/Walloon/German population (Neth) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequent analyses were conducted using K\u0026thinsp;=\u0026thinsp;3 clusters (Bel, BelNeth, Neth). Our dataset showed some limitations when we looked into further subdivisions due to the small number of loci used and the low allelic variability of these markers. Based on our knowledge of the biology of the species, we decided to conduct subsequent analyses with K\u0026thinsp;=\u0026thinsp;6 clusters (Bel_1, Bel_2, BelNeth_1, BelNeth_2, Neth_1, Neth_2) as defined by STRUCTURE analyses leaving the possibility of other genetic clusters that can only be confirmed by additional markers (e.g. SNPs) and sampling in the area.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eContemporary gene flow inferred from BayesAss was lacking between the genetic clusters for K\u0026thinsp;=\u0026thinsp;3 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The trend was similar at K\u0026thinsp;=\u0026thinsp;6, except for Bel_1 where we observed a fraction of 21.4% derived from Bel_2, while there was no migration from Bel_1 to Bel_2. More specifically, migration ancestry for the Bel_1 individual showed that they all have a high probability (\u0026gt;\u0026thinsp;0.98) of being first generation migrants from Bel_2, while all individuals from Bel_2 have a high probability of being non-migrants.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBayesAss analysis of migration rates among and within the genetic clusters as identified by STRUCTURE. Means of the posterior distributions of migration rate are shown and represent the fraction of individuals from subpopulation i that are migrants derived from subpopulation j. The rows list the subpopulations from which the individuals were sampled (subpopulations into which individuals migrated). The columns list the subpopulations from which the individuals migrated. Standard deviations for all distributions were \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eMigration from\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e\u003cp\u003e\u003cb\u003eMigration to\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eK\u0026thinsp;=\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eBel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003eBelNeth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNeth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.9927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.0037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelNeth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.0065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.9865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.0070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.0122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.0201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.9670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eK\u0026thinsp;=\u0026thinsp;6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eBel_1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eBel_2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eBelNeth_1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eBelNeth_2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNeth_1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eNeth_2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBel_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0227\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBel_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelNeth_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeth_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.8517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2) Genetic diversity\u003c/h3\u003e\n\u003cp\u003eLinkage Disequilibrium (LD) was significant for one pair of loci (MAV1 and MAV32) in one subpopulation (Bel_2) (p-adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary File 7). The significance of LD for this pair of loci in this subpopulation might be due to the presence of another genetic cluster that is yet to be confirmed by additional markers. LD can appear when mixing individuals from subpopulations with different allele frequencies. All loci were therefore used for downstream analyses.\u003c/p\u003e\u003cp\u003eFor K\u0026thinsp;=\u0026thinsp;3, allelic richness varied from 2.51 (Bel cluster) to 3.41 (Neth cluster) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HWE significantly deviated for all three genetic clusters, however this is expected for a spatial Wahlund effect. All Fis values fell within the confidence interval (Supplementary File 7), and therefore we tested whether any genetic cluster was more inbred than the two others. Our analyses showed that the inbreeding coefficient estimated for the BelNeth cluster was significantly higher than for the Bel cluster.\u003c/p\u003e\u003cp\u003eFor K\u0026thinsp;=\u0026thinsp;6, allelic richness varied from 1.96 to 2.69 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Not all loci were polymorphic in each genetic cluster. MAV7 was monomorphic in Bel_1, Bel_2, and BelNeth_1. CM40 was monomorphic in Bel_1 and Neth_2. Bel_1 had the lowest genetic diversity with a total number of alleles of 19 and an allelic richness of 1.96, while Neth_2 with the same number of individuals was highly polymorphic with 27 alleles and had the highest allelic richness (2.69). Bel_2 and BelNeth_1 deviated from HWE, but we suspect that this could be due to a spatial Wahlund effect. The Fis values were really high for BelNeth_2 and Bel_1, but still fell within the confidence interval (Supplementary File 8). We therefore tested whether any of the six genetic clusters were more inbred than the others. Pairwise comparisons showed that two subpopulations (BelNeth_2 and Neth_2) had non-overlapping confidence intervals and the inbreeding coefficient estimated for BelNeth_2 was significantly higher than for Neth_2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics for the nine microsatellite loci among the hazel dormouse subpopulations for K\u0026thinsp;=\u0026thinsp;3 and K\u0026thinsp;=\u0026thinsp;6. N: number of individuals, A: number of alleles, Ar: allelic richness, H\u003csub\u003eo\u003c/sub\u003e: observed heterozygosity, H\u003csub\u003ee\u003c/sub\u003e: expected heterozygosity, HWE_BH: probability values of concordance with Hardy-Weinberg expectations with corrected p-value (BH), Fis [CI]: global Fis with the lower and upper 95% confidence intervals.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH\u003csub\u003eo\u003c/sub\u003e/H\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHWE_BH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFis [CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.32/0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.07 [0.004, 0.14]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelNeth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.35/0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25 [0.17,0.33]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45/0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.12 [0.006,0.23]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eK\u0026thinsp;=\u0026thinsp;6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBel_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.23/0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.24 [-0.004, 0.43]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBel_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33/0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.02 [-0.04, 0.09]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43/0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.07 [-0.11, 0.22]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelNeth_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31/0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.19 [0.08, 0.29]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.46/0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.007 [-0.2, 0.13]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeth_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.42/0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.10 [-0.28, 0.08]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe FST and DJost estimates for K\u0026thinsp;=\u0026thinsp;3 and K\u0026thinsp;=\u0026thinsp;6 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For K\u0026thinsp;=\u0026thinsp;3, we observed a high genetic differentiation and low gene flow between the clusters. The highest pairwise value was recorded between clusters Bel and Neth (DJost\u0026thinsp;=\u0026thinsp;0.20). Genetic differentiation was higher between Bel and BelNeth (0.17) than between BelNeth and Neth (0.14). FST values showed that the gene flow was really low. Highest pairwise values were found between Bel and BelNeth (0.29), and between Bel and Neth (0.30). Gene flow seemed to be lower between Bel and BelNeth (0.29) than between BelNeth and Neth (0.23).\u003c/p\u003e\u003cp\u003eFor K\u0026thinsp;=\u0026thinsp;6, highest genetic differentiations (DJost\u0026thinsp;\u0026gt;\u0026thinsp;0.2) were observed between Bel_1 and BelNeth_1, Bel_1 and Neth_2, Bel_2 and BelNeth_1, Bel_2 and Neth_2, BelNeth_1 and Neth_1, and BelNeth_2 and Neth_2. Genetic differentiation was higher between Bel_2 and BelNeth_1 (DJost\u0026thinsp;=\u0026thinsp;0.24) than between BelNeth_2 and Neth_1 (0.13). Pairwise estimates of FST showed that gene flow was really low (\u0026gt;\u0026thinsp;0.2) between all genetic clusters, except for Bel_1 and Bel_2 that had a mean value of 0.14. Bel_1 and Bel_2 also had the lowest genetic differentiation (DJost\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePairwise DJost (above diagonal) and FST (below diagonal) estimates calculated for K\u0026thinsp;=\u0026thinsp;3 and K\u0026thinsp;=\u0026thinsp;6.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBelNeth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eNeth\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e[0.14\u0026ndash;0.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003cp\u003e[0.15\u0026ndash;0.28]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelNeth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003cp\u003e[0.27\u0026ndash;0.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e[0.11\u0026ndash;0.19]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003cp\u003e[0.27\u0026ndash;0.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003cp\u003e[0.19\u0026ndash;0.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBel_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBel_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBelNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBelNeth_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNeth_2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBel_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003cp\u003e[0.01\u0026ndash;0.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003cp\u003e[0.21\u0026ndash;0.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003cp\u003e[0.08\u0026ndash;0.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e[0.07\u0026ndash;0.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003cp\u003e[0.18\u0026ndash;0.35]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBel_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e[0.10\u0026ndash;0.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003cp\u003e[0.18\u0026ndash;0.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003cp\u003e[0.11\u0026ndash;0.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e[0.09\u0026ndash;0.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003cp\u003e[0.29\u0026ndash;0.44]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003cp\u003e[0.31\u0026ndash;0.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003cp\u003e[0.34\u0026ndash;0.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e[0.06\u0026ndash;0.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e[0.19\u0026ndash;0.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003cp\u003e[0.12\u0026ndash;0.30]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelNeth_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003cp\u003e[0.29\u0026ndash;0.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003cp\u003e[0.32\u0026ndash;0.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003cp\u003e[0.20\u0026ndash;0.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003cp\u003e[0.09\u0026ndash;0.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003cp\u003e[0.18\u0026ndash;0.29]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeth_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e[0.21\u0026ndash;0.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003cp\u003e[0.28\u0026ndash;0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003cp\u003e[0.27\u0026ndash;0.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003cp\u003e[0.27\u0026ndash;0.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003cp\u003e[0.08\u0026ndash;0.23]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeth_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003cp\u003e[0.35\u0026ndash;0.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003cp\u003e[0.40\u0026ndash;0.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003cp\u003e[0.24\u0026ndash;0.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003cp\u003e[0.34\u0026ndash;0.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003cp\u003e[0.24\u0026ndash;0.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3) Divergence time\u003c/h2\u003e\u003cp\u003eOur DIYABC analysis provides a preliminary estimate of divergence time, as we acknowledge the limited power of our dataset. To mitigate over-interpretation, we focused on the relative timing of divergence events and their biological plausibility, rather than precise dating. Future studies incorporating genome-wide SNP data would provide more robust estimates which are beyond the scope of this paper.\u003c/p\u003e\u003cp\u003eAnalysis with DIYBAC identified scenario 10 as the most probable, with moderately strong posterior probability (0.5376) (Supplementary File 9, A). This scenario suggests that two parental subpopulations with constant effective size diverged around 272 generations ago [CI: 57\u0026ndash;758] (i.e. 136 years ago [28\u0026ndash;379]) and got admixed around 122 generations ago [CI: 8-350] (i.e. 61 years ago [4-175]), giving birth to a third subpopulation. The representation of demographic scenario 10 and parameter estimates (Ne: effective population size [confidence interval], T: generations, ra: admixture rate, Na: ancestral population of size NA) for hazel dormouse subpopulations for K\u0026thinsp;=\u0026thinsp;3 (Bel, BelNeth, Neth) is available in Supplementary File 10. Population size was estimated at N\u0026thinsp;=\u0026thinsp;254 [65\u0026ndash;760], N\u0026thinsp;=\u0026thinsp;355 [63\u0026ndash;914], and N\u0026thinsp;=\u0026thinsp;620 [188\u0026ndash;974] for N1 (Bel), N2 (BelNeth), and N3 (Neth) respectively. The pre-evaluation scenario prior combination showed that the model (scenario and parameter prior definition) was not off the target (Supplementary File 9, B). Model checking analyses showed as well that the chosen model/posterior correctly explains the observed dataset (Supplementary File 9, C). The type I error (probability that scenario 10 is rejected although true) was 0.215, while the type II error (probability of choosing scenario 10 when it is not the true scenario) was 0.293 (293 counting decisions over 1,000 in favour of scenario 10). Although scenario 10 had a low type II error, this scenario is likely to be the most probable of all proposed scenarios.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e1) Genetic differentiation as a result of past human activities\u003c/h2\u003e\u003cp\u003eOur results suggest that the landscape management practices from the last century had an impact on the current genetic structure of the hazel dormouse population in the Meuse-Rhine Euregion. The hazel dormouse is an arboreal rodent that is strongly associated with deciduous or mixed deciduous-coniferous forests that have a well-developed understory (Juškaitis \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Juškaitis and B\u0026uuml;chner \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Goodwin et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The interaction between cultural development and the natural environment has influenced the European forest composition and structure since the last ice age (Brewer et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bradshaw \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Berglund et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Overballe-Petersen et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In Northwest Europe, the Holocene is characterised by different periods of long-term deforestation/regeneration patterns (Berglund \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Demographic pressure due to the establishment of cities and intensive agriculture due to a climatic amelioration led to woodland clearance to create more farmlands (Berglund \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kaplan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In Europe, forest areas reached an all-time low around 1850 (e.g. only 2% of the original forests remained in the Netherlands, Mohren and Vodde \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) after which European countries experienced forest transitions (net reforestation) (Kaplan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; McGrath et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These events have probably acted as a controlling factor on the distribution of arboreal species such as the hazel dormouse. We estimated a first separation between the Bel (SL1/SL2) and the Neth clusters (SL4) around 136 years ago, admixing into the third subpopulation BelNeth (SL3) around 61 years ago. A comparison between the present situation and a detailed historical map from 1867 (Kadaster \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) does not show major changes in size and connectivity of the forests at first sight (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the presence of more stepping stones, better developed hedges and hedgerows, a wider distribution and probably higher densities of hazel dormice due to higher habitat quality, may all have contributed to a higher exchange rate of individuals between the forests in the past compared to now. Forests were somewhat larger in 1867, with more forest patches connecting BelNeth_2 and Neth_1. In 1867, the forests within Bel_2 (SL2) were larger and better connected, forming a broad forest belt. Bel_2 (SL2) was larger on the east side. This part of the forest gradually disappeared, temporarily increasing the distance between Bel_2 (SL2) and BelNeth_1 (SL3). The construction of the railway during the First World War led to the development of the eastern railway verges that now form a large part of the connection between Bel_2 and BelNeth_1. Hazel dormice were also previously present in the forests west-northwest of Bel_2, some of which remain connected to Bel_2 but have experienced such severe habitat degradation that they can no longer support a hazel dormouse subpopulation. So there may have been a more important connection between Bel_2 and BelNeth in the past, largely formed by these forests and the green belt between them and BelNeth_2 shown on Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. From the last quarter of the 19th century, the traditional hedge landscapes in Western Europe began to deteriorate, but it was only in the period 1950\u0026ndash;1975 that the disappearance of mixed woodlands and pastures reached its peak (Van Driessche \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This decline was due to three anthropological factors: 1) introduction of barbed wire, 2) declining demand for firewood and convenience wood and 3) increase in scale and mechanisation of agriculture.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2) Genetic structure and diversity\u003c/h2\u003e\u003cp\u003eThe present study shows a significant genetic differentiation within the hazel dormouse population in the Meuse-Rhine Euregion, dividing it into spatially isolated subpopulations inhabiting poorly connected forest fragments, with low genetic exchange. This is further demonstrated by the virtually non-existent migration rate between the different patches. Similar results with strong genetic differentiation among populations have already been observed in hazel dormouse populations in Central Italy (Bani et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), but also for several other arboreal mammals, such as edible dormouse, squirrel glider, and ringtail possum (Taylor et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fietz et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lino et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study demonstrates low allelic richness in all surveyed hazel dormouse subpopulations (Ar from 1.96 to 2.69). Our results thus mirror previous studies that examined the impact of forest isolation on genetic structure of hazel dormouse populations, showing a significant genetic isolation of population fragments (Mills \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Naim et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bani et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Friebe et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A study on the effect of forest fragmentation on hazel dormice in Italy showed that the physical link between woodlots was the most important parameter to avoid isolation (Capizzi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Not only between, but also within forests connectivity will improve sustainability of hazel dormouse populations. Mortelliti et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) observed that in isolated habitat patches, linked trees and a scrub layer are important for successful hazel dormouse conservation. Another study in central Italy revealed that higher abundance of shrubs favoured higher abundance of individuals, while a higher diversity of resources had a direct positive effect on survival (Sozio et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The authors showed that regrowing forest represents the most suitable habitat for hazel dormice, while coppice (\u0026lt;\u0026thinsp;five years regrowing) was unsuitable although a study showed that hazel dormice used coppice again after 2\u0026ndash;3 years on the railway verges in Belgium (Verbeylen, pers.com). Goodwin et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Juškaitis (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) observed the same pattern for the UK and Lithuania respectively. All these studies showed that active management of woodland providing sufficient early successional habitats and other dense vegetation will benefit the species\u0026rsquo; conservation while it is still compatible with timber production.\u003c/p\u003e\u003cp\u003eIn addition to anthropogenic impact of forest management on arboreal species, studies showed that dispersal of hazel dormice is influenced by the presence of barriers such as roads or discontinuous hedgerows (Naim et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Friebe et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, Combe (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that the roads do not disrupt the dispersal but rather the width of the road and the continuity of the roadside habitat. Even barriers such as 30 m wide motorways can be crossed repeatedly though (Kelm et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and roadside shrubs are suitable habitats for reproduction (Friebe et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Bani et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) stressed the importance of improving the continuity and quality of hedgerows (e.g. by developing a dense and diversified shrub layer), as discontinuous hedgerows are unsuitable for hazel dormice and could represent an ecological trap for the species. Our study suggests that railway verges with scrubs can represent valuable connections between fragmented habitats. Where hazel dormice inhabit the Flemish railway verges, the typical management scheme was slightly adapted for their benefit, with the 4 m wide inner zone of dense scrub mowed in winter instead of summer, the 4 m wide central zone of diverse shrub species coppiced more small-scale, and the higher trees in the outer zone were only cut if necessary for safety reasons. This results in the continuous presence of all essential habitat elements within a narrow strip, provided the management scheme is adhered to and no additional detrimental maintenance practices are implemented. Consequently, the western railway verges\u0026mdash;part of the Bel_2 cluster\u0026mdash;harbor particularly high densities of hazel dormice and likely function as a source population for the surrounding forests. This may also account for the observed dispersal rates from Bel_2 to Bel_1 (see BayesAss analysis), despite the poor connectivity between these two clusters. A few individuals originating from BelNeth_1 were geographically located within Bel_2, and one disperser from Bel_2 was found in BelNeth_2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that the high-quality habitat along the railway corridor may facilitate colonization of new areas. Field data indicate that minor interruptions in woody vegetation do not constitute significant barriers for hazel dormice, as individuals have been observed to cross open spaces over distances of up to 500 m (B\u0026uuml;chner \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lemmers et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Whether they do this or not, will also depend on how much pressure they suffer (e.g. if all space in their patch of birth has been taken, they are forced to settle elsewhere). And not only during dispersal, but also during daily home range use, roads, railways and railway bridges can be crossed often (Verbeylen et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lemmers et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3) Conservation advice\u003c/h2\u003e\u003cp\u003eHazel dormouse densities in BelNeth_1 are much lower than in Bel_2 based on the number of shrub nests found during the annual monitoring and the amount of suitable edges and other high-quality habitat in the form of dense thicket (Verbeylen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This could explain the relatively large inbreeding coefficient for BelNeth_1 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The population size estimated with DIYABC for Bel and BelNeth is quite similar, while the population size for Neth is larger. This is likely due to the connection to the adjacent German and Walloon forests. While Neth_1 and Neth_2 do not seem to suffer from inbreeding depression and have a decent population size, maintaining a heterogeneous habitat and active woodland management is still essential for the survival of the species in this cross-border region. The highest priority is to increase hazel dormouse densities in BelNeth (through habitat improvement) and to connect it to the surrounding populations Bel_2 and Neth_1. The importance of the connection between BelNeth and Bel_2 was already stressed in 2013 (Verbeylen et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and in 2017, it was incorporated in the Flemish hazel dormouse species protection plan (Nijs and Verbeylen \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and is now being put into practice. Further, we strongly suggest the use of higher-resolution data such as the SNP panel (Beez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for future genetic monitoring of the species.\u003c/p\u003e\u003cp\u003eIn conclusion, our study indicated low genetic diversity associated with low gene flow between subsequent hazel dormouse subpopulations in the Meuse-Rhine Euregion, which might impede adaptive responses to any future deterioration of their natural environments. From a conservation point of view, this includes the development and maintenance of corridors in the matrix between woodlands and small habitats in the landscape as well as corridors between habitats inside forests. This will restore habitat connectivity, improve forest habitats and prevent further genetic erosion for the hazel dormouse population in the cross-border region. The current action plans for Flanders (Nijs and Verbeylen \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the Netherlands (Lemmers et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), aiming to further improve and restore habitat connections, are being implemented and will make the hazel dormouse metapopulation more robust. We expect that ecologically functioning corridors between the hazel dormouse subpopulations in the Meuse-Rhine Euroregion will significantly decrease inbreeding risks within each of these subpopulations, favouring their long-term survival.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003eSampling was licensed by local or regional authorities and samples were collected under permission of the Dutch Fauna and Flora Act (license number FF/75A/2012/037 of the Dutch Mammal Society) and the Decree of the Flemish Government on species protection and species management (license number ANB/BL-FF/V11-00123 of Natuurpunt Studie).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cp\u003eAll authors approved the version to be published.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by a research credit of the Fonds de la Recherche Scientifique \u0026ndash; FNRS to Johan Michaux and Alice Mouton was a FRIA grantee (Fonds pour la Formation \u0026agrave; la Recherche dans l'Industrie et dans l'Agriculture) of the Fonds de la Recherche Scientifique \u0026ndash; FNRS at the time of research. The field work was partially supported by a biodiversity project of the Flemish Province of Limburg, and the Agency for Nature and Forests of the Flemish Government (ANB), the Research Institute for Nature and Forest (INBO), Natuurpunt Studie (Research Department of Natuurpunt), Dienst Landelijk Gebied (DLG) as part of the Dutch Ministry of Economic Affairs and by Interreg project \u0026lsquo;Interreg IV Maas-Rijn Habitat Euregio\u0026rsquo; (subproject \u0026lsquo;recovery plan hazel dormouse\u0026rsquo;).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAM and JM conceived the project and designed the research. GV, RK coordinated the sample collection efforts. GV, RK provided and/or collected samples. AM conducted the laboratory work, the analyses and interpretation of the genetic data. PL prepared figure 1,3. AM wrote the manuscript with input from all coauthors, particularly for the Sampling and Study Area (Materials and Methods) and Discussion sections. All coauthors reviewed and commented the manuscript. All authors approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eIn Flanders, sample collection by the Mammal Working Group of Natuurpunt was mainly volunteer work. For the Netherlands, Wallonia and Germany, samples were collected during the Interreg project \u0026lsquo;Interreg IV Maas-Rijn Habitat Euregio\u0026rsquo; (subproject \u0026lsquo;recovery plan hazel dormouse\u0026rsquo;) by the Dutch Mammal Society, Natuurbalans \u0026ndash; Limes Divergens and Natuurpunt Studie. We would like to thank the following people for assisting and enabling us to conduct this study: Martijn Dorenbosch, Gerald Driessens, Ruud Foppen, Griet Nijs, Rik Palmans, Rian Pulles, Jip Ramakers, Rick Reijerse, Sander van de Koppel, Wim van Mourik, Ivo Vanseuningen, Dominique Verbelen and Ludy Verheggen. ANB, INFRABEL, Limburgs Landschap, Natuurmonumenten, Staatsbosbeheer, Stichting ARK and several municipalities and private landowners granted permission to access the study sites.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBani L, Orioli V, Pisa G et al (2017) Population genetic structure and sex-biased dispersal of the hazel dormouse (\u003cem\u003eMuscardinus avellanarius\u003c/em\u003e) in a continuous and in a fragmented landscape in central Italy. 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Genetics 163:1177\u0026ndash;1191\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"arboreal rodent, conservation management, Gliridae, habitat fragmentation, microsatellite marker","lastPublishedDoi":"10.21203/rs.3.rs-6545691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6545691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe survival of many species has been impacted dramatically by human activities over the last few centuries. More specifically, forest fragmentation, due to human settlement and development of agriculture, has highly affected arboreal species. Understanding patterns of genetic structure of endangered species occupying fragmented forest habitats is a requirement for appropriate conservation management, especially for species with low dispersal abilities like the hazel dormouse (\u003cem\u003eMuscardinus avellanarius\u003c/em\u003e). The hazel dormouse is an arboreal rodent that is strictly protected in Europe. The aim of this study conducted from 2004 to 2011 was to investigate population fragmentation of the hazel dormouse in part of the Meuse-Rhine Euregion using microsatellite markers. The results revealed a significant genetic differentiation and low gene flow between subpopulations. Fragmentation of the hazel dormouse population in this region seems to have occurred during the 20th century, suggesting that active management to improve habitat quality, amount of habitat and connectivity is essential to impede future genetic erosion. Furthermore, our results show that railway verges, like other linear habitat elements, cannot only be permanently inhabited, but also act as a valuable corridor, connecting subpopulations and allowing the colonisation of new sites. Finally, in response to our study, both Flemish and Dutch hazel dormouse species protection plans included the objective of providing a functional ecological corridor between several of the subpopulations, which has already been partially implemented.\u003c/p\u003e","manuscriptTitle":"Population-genetic structure of the hazel dormouse Muscardinus avellanarius in the Meuse-Rhine Euregion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-18 05:33:23","doi":"10.21203/rs.3.rs-6545691/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e388e1d-ef5c-4dbc-b5e3-0ae1384fff64","owner":[],"postedDate":"October 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T14:10:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-18 05:33:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6545691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6545691","identity":"rs-6545691","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}