Asymmetric gene flow and genetic admixture underscore the importance of landscape connectivity in himalayan black bears and leopards

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This study investigates the population genetics of Himalayan black bears ( Ursus thibetanus ) and leopards ( Panthera pardus ) in Himachal Pradesh, a Indian State experiencing escalated anthropogenic influences and large scale development. Using microsatellite markers and a combination of Bayesian clustering, discriminant analysis of principal components (DAPC), and spatial genetic analyses, we evaluated the patterns of genetic diversity, population structure, gene flow and evidence of isolation by distance (IBD) for these species. The results showed a relatively low to moderate level of genetic diversity, with Ho = 0.35 for black bears and Ho = 0.41 for leopards, and evidence of historical genetic bottlenecks but stable contemporary effective population sizes for both study species (204.5 for black bears, 208.1 for leopards). Furthermore, we found moderate genetic differentiation, high admixture and asymmetric gene flow across the study region for both the study species, with no significant isolation by distance (IBD). The findings of this investigation highlight the resilience of these species while emphasizing habitat connectivity as the critical factor for preserving genetic diversity. Implementation of conservation strategies such as wildlife corridors and habitat restoration to alleviate fragmentation and sustain populations over time is recommended. This research established a basis for subsequent studies focused on genetics and ecology, and improved the comprehensive understanding of wildlife conservation within the Himalayan ecosystem. Landcape connectivity Gene Flow Resilience Himalayas Carnivores Himalayan Black Bear Common Leopard Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Historically, human land use change has led to the reduction and fragmentation of wildlife habitats globally and had a substantial impact on several wildlife populations (Fischer & Lindenmayer, 2007; Saunders et al., 1991). Anthropogenic habitat loss and fragmentation followed by climate change are known to be significant threats to wildlife across the globe and impact the genetic diversity and population genetic structure of wildlife populations by causing the subdivision and isolation of large and contiguous populations (Frankham et al., 2002; Méndez et al., 2011). The combined effect of reduced gene flow among the subgroups, inbreeding and stronger genetic drift leads to reduced genetic diversity, and species fitness, restricted dispersal ability and increased risk of local extinction (Wright 1949; Slatkin 1977; Frankham et al., 2002; Spielman et al. 2004; Hogg et al., 2006; Ripple et al., 2014). Consequently, such isolated populations with low levels of genetic diversity are less able to adapt to adverse environmental changes in the era of increased anthropogenic activities and climate change (Bürger and Lynch 1995; Keller and Waller 2002; Bijlsma and Loeschcke 2012). Himalayan ecosystems possess complex topography, a wide range of habitat and environment gradients supporting several conservation important species which make this landscape sensitive to climate change and habitat fragmentation. This region is also important in terms of elusive species diversity yet least explored to understand the distribution, habitat ecology, genetic diversity and population genetic structure to inform effective conservation and management (Joshi et al. 2022; Joshi et al. 2020; Dahal et al. 2023). In addition, landscape-level population genetic studies of mammals are lacking from the Himalayas to inform the effective population monitoring for long-term species survivability, mitigate human-wildlife conflict and suggest management recommendations (Joshi et al. 2022; Kumar et al. 2022). Among mammals, large carnivores are particularly prone to these effects due to their extensive ranges in the Himalayas, low population densities, and long generation times (Noss et al., 1996). Further in the past two decades’ bear-human and leopard–human conflict drastically increased in the Himalayan states considering multiple factors demand to understand the movement of species, population growth trends and genetic diversity (Kumar et al. 2022; Naha et al. 2020). Despite their long-dispersal abilities, human development disrupts the gene flow of several large carnivore populations across the globe such as tigers and leopards in India (Thatte et al., 2020), pumas and American black bears in North America (Cushman and Lewis 2010; Ernest et al. 2014), Eurasian lynx in Europe (Bull et al. 2016), and jaguars in Mexico and Brazil (Roques et al., 2016). As a result, many large carnivore populations have become functionally isolated, leading to rapid declines in population size and increased inbreeding, with heightened extinction risks (Frankham et al., 2002; Crooks, 2002; Cushman & Lewis, 2010; Ernest et al., 2014; Thatte et al., 2020). Further, the Himalayas are sensitive to climate change and past studies have shown how past climatic events impact the population structure of species, and thus it is imperative to forecast how future climatic shifts will affect the genetic structure of species for effective conservation planning (Carnaval & Bates, 2007; Jay et al., 2012; Lima et al., 2017). The genetic variation within a species reflects the effects of gene flow, genetic drift and natural selection influenced by geographic distance and environmental factors. Therefore, it is essential to understand the genetic variation to reveal the demographic history and population structure (Lee & Mitchell-Olds, 2011; Eckert et al., 2008; Sork, 2016). Considering the complex topography wide elevation range provide wide environment gradients and along with geographic distance this could be one of the key factors contributing to genetic variation within the species, and likely affect the distribution of allele frequencies, gene flow and genetic structure in populations (Sork, 2016; Wang, 2013; Smith et al., 1997; Sacks et al., 2004; Freedman et al., 2010; Freeland & Kloepper, 2010; Thomassen et al., 2010). Therefore, for effective long-term conservation of species, it is essential to understand the role of environmental factors in shaping the gene flow and genetic structures, how habitat fragmentation and anthropogenic activities restrict the species movement as well as to disentangle their importance to geographic distance and barriers (Pease et al., 2009; Freedman et al., 2010; Manel et al., 2010; Sork et al., 2010). Among the carnivores, Himalayan black bear distribution is restricted to Himalayan states, while the leopard is found in a much wider range of habitats, extending across diverse habitats, from the foothills of the Himalayas to other parts of the Indian subcontinent. Himalayan black bears primarily prefer mixed broadleaf and coniferous forests, as well as alpine scrub, with a preference for elevations between 1200 to 3300 meters (Sathyakumar, 2001; Aryal et al., 2012). Similarly, leopards are highly adaptable carnivores inhabiting in a wide range of habitats, from tropical forests and dry scrublands to temperate forests and sub-alpine zones, usually up to 3000–3500 meters (Lovari et al., 2013; Stein et al., 2020). Over the last few decades, anthropogenic pressures like poaching, habitat degradation, fragmentation as well as escalating human-wildlife conflict have resulted in population declines and range contractions for both species (Ripple et al., 2014). Therefore, conservation efforts focussing on maintaining landscape connectivity, mitigating conflict and reducing anthropogenic pressures are critical for ensuring the long-term survival of these species in the Himalayan region. Given that large carnivores require extensive, continuous habitats for survival, despite their high dispersal ability, gene flow is often negated by human disturbances (Crooks, 2002). Habitat loss, fragmentation and hunting driven by humans and climate change have led to significant population declines in these species over the past century (Nowell & Jackson, 1996; Perez, 2001). However, genetic studies of these species are limited, often due to challenges of obtaining the samples, leaving the research on their genetic patterns and response to environmental and human-related landscape disturbances poorly understood in many regions globally. In this study, we aim to fill this knowledge gap by examining two large carnivores’ leopards and Himalayan black bears in the fragile Himalayan region, where human-induced and climate-driven changes have dramatic impacts. These species were chosen for their contrasting ecological requirements, leopards are highly adaptable to a wide range of habitats including disturbed areas (Balme et al., 2013; Jacobson et al., 2016). In contrast, Himalayan black bears are primarily forest dwellers, relying on seasonal food availability, making them more sensitive to habitat fragmentation (Cushman and Lewis, 2010). These ecological differences make them ideal species for understanding how environmental factors and human and climate-induced disturbances shape the genetic patterns and gene flow in large carnivores of this region. The key to biodiversity conservation lies in understanding the ecological web, this study aims to contribute to that understanding by studying two key players in the Himalayan region’s biodiversity. Methodology Study area Himachal Pradesh, lies between 30°22' N and 33°12' N and 75°47' E and 79°04' E, and is a part of the ecologically significant and fragile Western Himalayan region (Fig. 1 ). The region is ecologically rich and shows a great variation in elevation, ranges from 350 to over 6,000 m. The climate of this region varies significantly with elevation, from subtropical to alpine regions, heavily impacted by extreme temperatures and monsoonal rains (Sharma & Chettri, 2005). This topographical and climatic variation fosters a wide variety of forest types, including subtropical, temperate, and alpine forests, and likely provides homes to a wide range of wildlife species, including leopards, black bears, snow leopards, and numerous ungulate species (Saberwal, 1996; Negi, 1990). The interaction of topography, climate and habitat fragmentation in this region makes it an ideal place for studying how these climatic factors shape the genetic patterns and gene flow in species with discrete ecological requirements (Thakur et al., 2020; Sathyakumar, 2001). This study is particularly important for comprehending the broader effects of environmental factors on wildlife populations and for establishing efficient conservation initiatives. Study design, Sample collection and DNA extraction We used a grid-based sampling strategy to collect the non-invasive (scats) genetic samples of the study species (i.e., Common leopard and Himalayan black bear) across the study region. We first divided the study area into 5 × 5 km sampling units (grid cells) based on the home range size of the study species. Then we selected the sampling grids based on the forest cover of the study landscape, likely the potential habitats of the study species. A total of 1514 grids were selected in different forest ranges of Himachal Pradesh both inside and outside protected areas. Then in each sampling grid, non-invasive genetic samples were collected opportunistically as well as systematically along animal and manmade trails (n = 3, with a maximum length of 3 km) in the study area. All the field activities were conducted in collaboration with the forest department. Altogether 700 samples of black bear and 1200 samples of common leopard were collected across the study region. Then genomic DNA was extracted from the suitable samples using the Qiagen Kit method. Following DNA extraction, the samples were screened for species identification using species-specific Mitochondrial control region (For black bears: Taberlet and Bouvet 1994; and leopard: Hyun et al., 2020). Microsatellite selection, screening and genotyping To assess the population genetic patterns of our study species, 20 and 15 highly polymorphic microsatellite markers were screened for Himalayan black bears and leopards, respectively following the necessary criteria suggested by several non-invasive genetic studies (Waits et al. 2001; Bellemain and Taberlet, 2004). These markers were widely used globally in black bear and leopard genetic studies. However, based on PCR amplification success rate, 12 and 10 loci were selected to genotype all the field-collected samples of Asiatic black bear and leopard, respectively. Multiplex panels were designed to include the full set of loci for each species. The description of all the microsatellite markers used for black bear and leopard are given in supplementary Tables S1 and S2, respectively. The PCR reaction of each multiplex was setup with a 10 µl reaction volume containing 5 µl of 2X Multiplex MasterMix with HotStart Taq Polymerase (Qiagen), 2 µm BSA, 0.2 µm, 1 µl of forward fluorescence labelled primer (10µM) cocktail and 2 µl of extracted DNA. The PCR conditions were as follows: initial denaturation (95°C for 15 min), 40 cycles of denaturation (94°C for 35 s), annealing (varied between 50°C – 62°C for 1 min) and extension (72°C for 90 s) and a final extension (70°C for 30 min). All the PCR products were genotyped using capillary electrophoresis on an ABI 3500XL sequencer and GeneScan–500 LIZ® as the Size Standard (Applied Biosystems, Carlsbad, California). The alleles were scored using the program GeneMapper 3.7 (Applied Biosystems), and validated through visual inspection. Genotyping error rates, data validation and genetic variation To ensure genotyping accuracy, each sample was genotyped thrice following a multi-tube approach (Miquel et al., 2006), and accepted the heterozygote only if we encountered two alleles in two attempts. The maximum likelihood of allele dropout (ADO) and false allele (FA) error rates were calculated using PEDANT v1.0, with 10,000 search steps to enumerate each error rate (Johnson and Haydon, 2007). The frequency of null alleles was estimated using the program FreeNa (Chapuis and Estoup, 2007). The scoring errors in the data were assessed and validated in MICROCHECKER 2.2.2 (Van Oosterhout et al., 2004). The cumulative PID (probability of identity) and PID (sibs) were calculated using GenAlEx v6.0 (Peakall and Smouse, 2012) to measure the power of a selected panel of markers to distinguish individuals. The summary statistics (i.e., number of alleles per locus, observed heterozygosity, expected heterozygosity, and inbreeding coefficient) were estimated using GenAlEx v6.0 (Peakall and Smouse, 2012). Tests for deviation from Hardy-Weinberg Equilibrium (HWE) and linkage disequilibrium for each locus were calculated using GenAlEx v6.0 (Peakall and Smouse, 2012). Population structure genetic structure Individual-based Bayesian approach implemented in program STRUCTURE 2.3.3 (Pritchard et al., 2000) was used to infer the population genetic structure and contemporary genetic processes in black bear and leopard populations across the study landscape. STRUCTURE is a non-spatial model-based Bayesian clustering approach and infers the population structure based on allele frequency at each locus in a sample. The analysis was performed for K = 2 to K = 10, with 20 replicates for each K. The STRUCTURE runs were performed using the admixture model and correlated allele frequencies with 10 5 burn-in and 10 6 Markov chain Monte Carlo iterations (MCMC), and sampling location as a priori. The optimum number of biological populations was inferred by using likelihood distribution L ( K ) and the delta K (Evanno et al., 2005), with the web version of Structure Harvester 5 v0.56.1 (Earl 2012). The assignment plot of STRUCTURE output was prepared using the program, DISTRUCT (Rosenberg 2004). Additionally, we carried out a Discriminant Analysis of the Principal Component (DAPC-Jombart et al., 2010) implemented in the R package adegenet (Jombart, 2008), to identify the clusters of genetically related individuals and the extent of allele sharing. DAPC is a multivariate model-free approach and is able to identify genetic clusters without any underlying population genetic assumptions, in particular Hardy-Weinberg equilibrium. DAPC explains the genetic variation by transforming the allelic data into uncorrelated components (PCA), and this makes DAPC more effective at identifying some complex spatial and hierarchical genetic structures (Evanno et al., 2005; Jombart et al., 2010; Kanno et al., 2011). Principal components (PCs) explaining > 90% of variance were identified to determine the maximum variation between clusters, obtained through 10 6 MCMC iterations. The number of PCs retained was calculated using a cross-validation method with 100 replicates, as implemented in the ‘xvalDapc’ function (Jombart and Collins, 2015). Effective population size and population bottleneck To check the genetic bottlenecks for the whole population of study species, two qualitative approaches were used viz. (a) the Garza-Williamson index (or M ratio) implemented in Arlequin v3.1 (Excoffier et al., 2005) and (b) the heterozygosity excess method implemented in Bottleneck v1.2.02 (Piry et al., 1999). The HET approach tests for heterozygote excess as compared with that expected under mutation − drift equilibrium. The significance of heterozygosity excess was determined by a one-tailed Wilcoxon test under three mutation models: Infinite Allele Model (IAM), Stepwise Mutation Model (SMM) or Two-phase Model (TPM). In TPM, 95% of single-step mutational steps (variance at 12%), with 10 4 iterations were used (Piry et al. 1999). Contemporary effective population size ( Ne ) was calculated using the linkage disequilibrium (Hill, 1981; Waples, 2006; Waples and Do, 2010) and molecular coancestry (Nomura 2008) methods implemented in the program NeESTIMATOR v.2.1 (Do et al. 2014). These two methods were used, as Wang et al. 2016 suggested that Ne estimates are sensitive to assumptions of different models. Given, that the Ne estimate characterizes the actual number of breeders in the entire population, but the estimates given by the above-mentioned methods reflect the effective number of breeders Neb , which do not always accurately resemble the Ne (Nomura 2008). In the linkage disequilibrium model, the rare alleles seem to bias the results, therefore rare alleles with frequency less than 2% were excluded ( PCrit = 0.02; see Waples and Do, 2010). Gene flow and migration rate The pairwise estimates of F st (Weir and Cockerham, 1984) was used as an indirect measure to determine the historical gene flow between populations of black bears and leopards. In addition, the rate of gene flow across the populations was expressed as the number of migrants per generation (Nm), where N is the effective population size and m is the proportion of migration per generation. Nm is calculated by (1/Fst-1)/4 (Wright 1984; Slatkin 1987). BayesAss v3.0 was used to calculate the recent migration rate (m - last 5–6 generations) between the populations of study species (Wilson and Rannala, 2003). Multiple simulations (n = 3) were carried out with different seed numbers using 10 7 MCMC iterations, of which 106 were discarded as burn-in periods. In addition, ‘detect migrants’ implemented in GENECLASS 2.0 was used to identify the first-generation migrants (i.e., individuals born in a population other than the one in which they were sampled). A Bayesian approach (Rannala and Mountain 1997) and the resampling method of Paetkau et al. (2004) were used with 10,000 simulated individuals at an alpha of 0.01. A likelihood ratio test was calculated and used to compare the population where the individual was sampled over the highest value among all populations ( L = L_home/L_max ). To further investigate the contemporary migratory patterns and directional relative magnitude between landscape populations, migration networks were generated using the DivMigrate function (Sundqvist et al., 2016) within the diveRsity R package (Keenan et al., 2013). The relative migration rates were calculated using G ST (Nei, 1973) and Jost D (Jost, 2008) as a measure of genetic distance with 1000 bootstrap repetitions. Isolation of distance (IBD) The patterns of gene flow between landscape regions for black bear and leopard populations were evaluated under the hypothesis of isolation by distance (IBD), assuming that black bear and leopard movement decisions in the study landscape are affected purely by geographic distance and hypothesize that genetic exchange occurs more between neighbouring individuals than distant Individuals. To assess the effect of isolation by distance on the genetic divergence of black bear and leopard, pairwise euclidean distance and genetic distance between each individual were calculated. The euclidean distance was calculated using a uniform raster with all grid cell values equal to 1 using the adegenet package in R (Jombart 2008). To calculate the genetic distance, we used the propShared function in the adegenet package of R (Jombart, 2008), and calculated the proportion of shared alleles (Dps) between each individual of the study species. Dps is a measure of similarity, whereas genetic distance is a measure of dissimilarity, therefore Dps and genetic distance are inversely related. Then IBD was determined through the correlation between the genetic distance (Dps) and the geographic distance (Euclidean) and was assessed using Mantel’s test in the adegenet package in R (Jombart, 2008). Pairwise spatial autocorrelation was carried out with GENALEX version 6.5 (Peakall & Smouse, 2012), and was compared across the scale of distance classes (5-300 km for both black bears and leopards). Correlograms of the correlation coefficients (r) for each distance class were used for visualization. The statistical significance through statistical processing of the null hypothesis of r = 0, 95% confidence intervals were generated for each distance class using 1000 simulations and 99 bootstrap repeats (Thatte et al., 2019). This method helped to explain the spatial patterns of genetic structure and the possible role of geographic distance in the genetic variation of the species. Spatial patterns of effective population size and genetic diversity To assess the spatial patterns of effective population size (NS) and genetic diversity under IBD, the standard diversity indices and NS were calculated based on the Wright-Fisher population and Wright’s genetic neighbourhood concept (Shirk and Cushman, 2011; Shirk and Cushman, 2014). The genetic neighbourhood radius was determined by the mean squared average parent-offspring dispersal distance. Then, moving window analysis was conducted using a neighbourhood radius of 20 km to map the NS and genetic diversity spatially. Results Genotyping error rates The genotyping error rates of microsatellite loci used for leopard and black bear are summarized in Tables 1 and 2 , respectively. The genotyping error rate varied among the loci used for leopard, with an average allelic dropout (ADO) rate and false allele (FA) of 0.20 and 0.03, respectively. The null allele frequency ranged between 0.15 and 0.29 with an average of 0.24 (Table 2 ). Similarly, the genotyping error rate varied among the loci used for Asiatic black bears, with an average allelic dropout (ADO) rate and false allele (FA) of 0.04 and 0.01, respectively. The null allele frequency ranged between − 0.05 and − 0.22 with an average of -0.09 (Table 1 ). Table 1 Genotyping error rates and genetic characterisation of Asiatic black bear at twelve microsatellite loci in Himachal Pradesh. S. No. Locus N Na Ho He Fis (W&C) ADO FA F(null) P ID (Cum) P ID sib (Cum) 1 G10H* H 284 19 0.261 0.92 0.72 0.1 0.0 -0.06 0.011 0.290000 2 MSUT3* H 274 17 0.321 0.92 0.66 0.0 0.0 -0.07 0.00013 0.085000 3 MSUT5* H 298 18 0.208 0.93 0.77 0.1 0.0 -0.06 0.0000013 0.025000 4 UT3 H 283 16 0.403 0.93 0.58 0.0 0.0 0.05 0.000000013 0.007100 5 UT4* H 285 13 0.509 0.89 0.43 0.0 0.0 -0.07 3.1E-10 0.002200 6 UT36* H 283 12 0.392 0.85 0.54 0.0 0.0 -0.06 1.2E-11 0.000740 7 MSUT2* H 313 9 0.393 0.84 0.54 0.0 0.0 -0.10 5.1E-13 0.000250 8 MSUT8* H 314 16 0.417 0.84 0.50 0.0 0.0 -0.12 2.1E-14 0.000086 9 UT1* H 286 10 0.301 0.84 0.63 0.1 0.0 -0.08 9.3E-16 0.000029 10 UT29* 278 10 0.403 0.81 0.49 0.0 0.0 -0.11 5.6E-17 0.000011 11 MSUT7* 321 9 0.374 0.78 0.54 0.0 0.0 -0.10 4E-18 0.000004 12 MSUT1* 293 9 0.239 0.56 0.56 0.1 0.1 -0.22 9.2E-19 0.0000021 Mean 292.67 13.16 0.35 0.84 0.58 0.04 0.01 -0.09 - - SE (±) 4.46 1.11 0.03 0.03 0.03 - - - Table 2 Genotyping error rates and genetic characterisation of leopard at ten microsatellite loci in Himachal Pradesh. S. No. Locus N Na Ho He Fis (W&C) ADO FA F(null) P ID (Cum) P ID sib (Cum) 1 FCA272* 294 12 0.46 0.88 0.48 0.12 0.08 0.28 2.7E-02 3.2E-01 2 PUN327* 318 11 0.34 0.85 0.60 0.02 0.00 0.29 1.1E-03 1.1E-01 3 FCA090* 293 11 0.34 0.81 0.58 0.30 0.05 0.25 5.7E-05 3.8E-02 4 FCA043* 289 9 0.36 0.80 0.55 0.00 0,02 0.29 3.7E-06 1.4E-02 5 PUN132* 317 8 0.31 0.77 0.59 0.22 0.03 0.21 3.4E-07 5.4E-03 6 FCA232* 332 8 0.53 0.77 0.31 0.29 0.04 0.27 3.1E-08 2.1E-03 7 FCA672* 289 5 0.40 0.65 0.37 0.29 0.04 0.18 5.3E-09 1.0E-03 8 PUN124 312 15 0.48 0.86 0.44 0.37 0.02 0.22 9 FCA628 338 10 0.49 0.87 0.44 0.12 0.04 0.15 10 PUN80 290 14 0.39 0.80 0.51 0.30 0.04 0.24 Mean 307.20 10.30 0.41 0.80 0.49 0.20 0.03 0.24 SE 5.90 0.94 0.02 0.02 0.03 Genetic variation For black bears, the cumulative probability of identity (PID biased) value of the microsatellite marker panel was 9.2 × 10 –19 , and the probability of identity (PID sibs) was 2.1 × 10 –6 (Table 1 ). Similarly, for the leopard, the cumulative probability of identity (PID biased) value of the microsatellite marker panel was 5.3 × 10 –09 , and the probability of identity (PID sibs) was 2.1 × 10 –6 (Table 2 ). A total of 307 and 333 unique genotypes of black bear and common leopard, respectively were identified across the study region. All the 12 microsatellite markers used for black bears were polymorphic, and the number of alleles at each locus ranged from 9 to 19, with a total of 158 alleles (Table 1 ). The global mean observed heterozygosity (Hobs) and expected heterozygosity (Hexp) were 0.35 ± 0.03 and 0.84 ± 0.03 across 12 polymorphic loci, respectively (Table 1 ). Similarly, all 10 markers used for leopard were polymorphic, and the number of alleles at each locus ranged from 5 to 14, with a total of 103 alleles (Table 2 ). The global mean observed heterozygosity (Hobs) and expected heterozygosity (Hexp) were 0.41 ± 0.02 and 0.80 ± 0.02 across 10 polymorphic loci, respectively (Table 2 ). Population structure The results based on the Bayesian approach implemented in STRUCTURE showed the maximum differentiation at K = 5 clusters for black bears and K = 3 clusters for leopards, as inferred by lnP(K) (Fig. 2 ). For black bears, the Chamba and Kangra populations showed a clear genetic distinctiveness with minimal sharing from the specific regions while other regions exhibit a high level of admixture, reflecting genetic contributions from diverse regions. For leopards, all populations display varying degrees of admixture, reflecting genetic contributions from multiple clusters. This pattern suggests a substantial level of genetic exchange and gene flow among the leopards across the different regions within the study area. To examine the genetic clustering using the multivariate approach, specifically discriminate analysis of principal component (DAPC), the number of PCs to be retained from optim.a.score function varied between 24 and 30 for black bears and 15 to 20 for leopards, while the cross-validation technique with 100 replicates estimated optimal PCs to be 30 for black bears and 20 for leopards. Therefore, 30 PCs were retained for the black bear and 20 for the leopard for the final analysis. The DAPC results showed a varied level of overlapping pattern with no clear clustering among the different regions within the study landscape for both the study species (Fig. 3 , 4 ), suggesting considerable genetic exchange within both species across the study region. Effective population size and population bottleneck The M-ratio (G-W index) calculated across polymorphic loci was below the threshold of 0.68, suggesting evidence of a historical genetic bottleneck in both black bear and leopard populations across the study region (Table 3 ). However, the heterozygosity tests (HET), conducted under the stepwise mutation model, did not detect any significant signs of recent genetic bottlenecks in both the study species. Additionally, a normal L-shaped allelic distribution was observed in both species, which is typically characteristic of populations that may not have experienced recent bottlenecks. Table 3 Summary of bottleneck analyses and effective population sizes (NE) of Himalayan black bear and leopard in Himachal Pradesh. Species Mutation model Wilcoxon test (H-excess) Allele frequency distribution M-ratio (G-W index) NE (CI95%) (Pcrit = 0.02) LD method Himalayan black bear IAM P = 0.0002 L-Shaped 0.36 ± 0.10 204.5 (182.9–230.5) TPM P = 0.0212 SSM P = 0.2348 Common leopard IAM P = 0.0004 L-Shaped 0.52 + 0.02 208.1 (175.4–251.4) TPM P = 0.0014 SSM P = 0.6152 IAM = infinite alleles model, TPM = two phase mutation model, SMM = stepwise mutation model and #significant p-value (p < 0.05) The estimates of contemporary effective population size, derived using linkage disequilibrium (LD) revealed high effective population size values for both species in this region. For black bears, the overall effective population size was estimated at 204.5 (95% CI: 182.9–230.5), while for leopards the effective population size was estimated at 208.1 (95% CI: 175.4–251.4) (Table 3 ). These results suggest that, despite the historical bottlenecks, the current effective population sizes for both species are relatively stable reflecting the recovery population dynamics. Gene flow and migration rate Low to moderate genetic distance measure (F ST , Table S3) was observed between the landscape regions for Himalayan black bear and leopard, therefore suggesting some gene flow between these landscapes. The recent migration analysis using BayesAss resulted in asymmetric gene flow in both the study species within the study region (Table S4, S5), with relatively higher gene flow within the region than between the regions. GENECLASS 2.0 identified a total of 18 individuals of black bears as first-generation migrants across the study landscape (Table S6). In contrast, only 6 individuals of leopards were identified as first-generation migrants across different regions within the study landscape (Table S7). The historical and contemporary directional migration rates per generation, estimated using the DivMigrate function within the diveRsity R package, indicated predominantly asymmetric gene flow between landscape region pairs for both Himalayan black bears and leopards (Figures S1 , S2, S3, S4). Isolation of distance (IBD) Spatial autocorrelation analysis conducted for Himalayan black bear and leopard populations using Mantel's test revealed that there was no significant correlation between genetic distance and geographic distance (Fig. 4 ). Thus, the IBD hypothesis seems not to be valid for these species in the study area. Redundancy analysis (RDA) results demonstrated that 5.22% of the total genetic variation was explained by spatial variables for leopards, while 7.09% of genetic variation was explained for black bears in this region (Figure S5). Spatial patterns of effective population size and genetic diversity The results showed a substantial difference in spatial genetic patterns under the IBD model for both the study species based on the average dispersal distance used in the analysis (Figures S6, S7). Our results indicated that areas with low, moderate to high levels of NS and genetic diversity indices were patchily distributed across the study region for both black bears and leopards. Discussion The genetics studies on Himalayan black bears and leopards have received less attention and remain relatively understudied in the Himalayan landscape compared to other carnivores across their global ranges. While numerous genetic studies have been carried out on black bears in North America (Onorato et al., 2004; Csiki et al., 2003), and leopards in Asia and Africa (Jacobson et al., 2016; Uphyrkina et al., 2001 Bhatt et al. 2020). No landscape-level genetic studies have been conducted to investigate the population genetics of these species in this region (Singh et al. 2021). Hence, for the first time, we attempted to evaluate the genetic composition of the two large carnivores at a fairly large landscape covering the entire State of Himachal Pradesh. The present study represents a foundational effort and an important attempt to fill this gap. Therefore, the findings of this study provide important insights toward knowing the genetic composition of these two species. Such information is essentially necessary for their effective long-term conservation planning, especially addressing the problems by human-induced habitat fragmentation in this region. The results revealed a moderate level of genetic diversity in both the Himalayan black bear and leopard populations in this region. For black bears, the global mean observed heterozygosity and expected heterozygosity were estimated at 0.35 ± 0.03 and 0.84 ± 0.03, respectively. Similarly, for leopards, the mean observed heterozygosity and expected heterozygosity were estimated at 0.41 ± 0.02 and 0.80 ± 0.02, respectively. The observed and expected heterozygosity values are lower than those reported in other studies on these species in different landscapes in India, such as for black bears (Ho = 0.58, Mukesh et al., 2015), and for leopards (Ho = 0.57, Mondol et al., 2009; Ho = 0.74, Dutta et al., 2013). Our findings on heterozygosity levels indicate moderate genetic variation compared to studies on these species globally. For example, the observed heterozygosity levels in North American black bears (Ho = 0.68, Csiki et al., 2003) and leopards in Africa and Asia (Ho = 0.57–0.71, Uphyrkina et al., 2001; Jacobson et al., 2016) are generally higher, reflecting less fragmented and larger population sizes in those landscapes. Further, the comparatively lower genetic diversity in black bears and leopards may reflect both historical and present pressures, that range from habitat fragmentation to human-wildlife conflict and restricted dispersal opportunities within the Himalayan topography. Similar to the genetic diversity patterns previously reported for other carnivore populations across the globe, including Kodiak Island brown bear (Ho = 0.26, Paetkau et al., 1998) and Gobi bear populations (Ho = 0.51, Tumendemberel et al., 2019). Furthermore, the positive inbreeding coefficient observed in both the study species indicates that the populations of these species in HP are inbred, which can be the putative reason for the moderate level of genetic diversity among them. Population genetic structure The Bayesian clustering results indicated moderate genetic differentiation in leopards and black bears across the study landscape. Notably, the Chamba and the Kangra populations of black bears exhibited distinct genetic profiles, suggesting minimal gene flow between these areas and the surrounding regions. Contrastingly, the other regions displayed substantially high admixture, indicating extensive gene exchange and population movement (Fig. 2 , 3 ). For the leopards, in general, the results by STRUCTURE revealed significant genetic intermixing admixture among all populations, indicating a high level of gene flow throughout the study area These results are consistent with previous studies showing the potential for gene flow in widely distributed species like leopards, by which they can sustain the genetic diversity even in fragmented landscapes (Jacobson et al., 2016; Uphyrkina et al., 2001). The Discriminant Analysis of Principal Components (DAPC) corroborated the results from STRUCTURE, showing substantial overlap and no clear population clustering, suggesting considerable genetic exchange across the study regions for both species. Such results indicate a dynamic population structure with little genetic isolation and support the idea of gene flow. The findings fit well with other large-home-range carnivores with wide dispersal capabilities, like the leopard and tiger, which can show little genetic differentiation even in the presence of habitat fragmentation (Gopal et al., 2012; Jhala et al., 2015). Isolation by distance We did not find a significant relationship between geographic distance and the proportion of shared alleles in both study species, demonstrating the absence of isolation by distance. This implies that in our study region, the genetic divergence cannot be solely explained by geographic distance in these two study species and corroborated other studies in the Himalayan landscape (Dahal et al. 2023). The lack of significant IBD is particularly interesting, considering the ability of these species to traverse large distances. However, habitat fragmentation brought on by human-caused factors like road development, human encroachment and deforestation may be the possible cause of the observed genetic differentiation in these species in this region. Indeed, these findings align with previous research on other carnivores, which demonstrated that habitat fragmentation often plays a key role in genetic structuring rather than geographic distance (Mondol et al., 2013; Shrestha et al., 2020). Interestingly, despite genetic differentiation, the limited population structuring observed in our study indicates that certain landscape features may be mitigating the impacts of fragmentation. The Himalayan region, although impacted by human activities in recent decades, still has largely intact habitats that maintain a level of connectivity and permit movement and gene flow between populations. This supports the idea that mountainous areas’ intact or semi-intact habitats can mitigate the consequences of habitat fragmentation (Schoville et al., 2012). On the other hand, if habitat fragmentation is certainly occurring, the timescale of such changes may not be long enough to leave observable genetic traces. Studies suggested that it can take multiple generations, often more than 50 years, for the genetic effects of habitat fragmentation to become evident (Epps & Keyghobadi, 2015; Landguth et al., 2010). Since genetic differentiation develops over an evolutionary timescale, our dataset may not fully reflect the impacts of recent anthropogenic fragmentation, underscoring the importance of continued monitoring of these species in the study region. Such observations accentuate the fundamental role landscape connectivity plays, which appeals to landscape connectivity strategies being the most suitable method for maintaining genetic diversity, particularly in those ecologically adaptable species, like a black bear or leopard that may occupy broad habitat ranges (Cushman et al., 2006; Proctor et al., 2015; Khosravi et al., 2018; Beier et al., 2008; Nawaz et al., 2014). Therefore, the conservation efforts in the region should be targeted towards wildlife corridors and habitat restoration that will probably minimize the impacts of habitat fragmentation on sustainable populations of species. Therefore, the conservation efforts in the region should be targeted towards wildlife corridors and habitat restoration that will probably minimize the impacts of habitat fragmentation on sustainable populations of species. Population bottleneck and effective population size Bottleneck analysis represented by the M-ratio index calculated over polymorphic loci indicated evidence of a historic genetic bottleneck in black bears and leopards in this region. This implies that the two species may have experienced population declines in the past, likely due to climatic events, habitat fragmentation or other anthropogenic pressures, as such bottlenecks are often linked with reduced genetic diversity. However, the absence of significant effects from the heterozygosity test under the stepwise mutation model and the fact that both species show a normal L-shaped allelic distribution indicate that the populations have probably recovered from these declines and are not currently experiencing a genetic bottleneck. These findings align with the hypothesis that populations that have recovered from past bottlenecks can eventually regain genetic diversity, especially in species with relatively high reproductive rates or when gene flow occurs between populations that have been fragmented (Frankham et al., 2002; Garcia-Dorado et al., 1999). In addition, the contemporary estimates of the effective population sizes, through linkage disequilibrium analysis, revealed that the Ne value for both species is pretty high: it is about 204.5 in black bears and 208.1 in leopards. This may suggest the absence of severe historical bottlenecks that could hamper the healthy levels of genetic diversity today. Such estimates are important for conservation strategies since they indicate a resilient population structure that can withstand the future challenges posed by climate change and habitat fragmentation (Charlesworth & Charlesworth, 2010). Conclusion This study provides considerable insight into the population genetics of the Himalayan black bear and leopard, species whose genetic frameworks have received very limited investigation in the Himalayan ecosystem. Results: Both species showed a moderate level of genetic diversity; the levels of observed heterozygosity were lower than those reported in other populations of these species found in other parts of the world. This diminished genetic diversity is ascribed to historical and continuing pressures, including habitat fragmentation and conflicts between humans and wildlife, which restrict both gene flow and opportunities for dispersal. The analysis of the population genetic structure indicated an average genetic differentiation among landscapes under study, with considerable gene movement between regions, especially for the leopards. The STRUCTURE and DAPC analyses pointed out the admixtures of genetic and fluid population structures in the study regions that underlie gene flow despite fragmentation in their habitat. Still, the lack of noticeable isolation by distance (IBD) relationship suggests that it is not the geographic distance alone that explains genetic divergence among the species studied. Conversely, habitat fragmentation caused by such human activities as road building and deforestation can more dramatically explain species' genetic differences. The bottleneck phenomena analyzed indicated the presence of pre-existing genetic bottlenecks in both species, likely resulting from previous fragmentation events. However, the currently high effective population sizes observed in both black bears and leopards and the lack of significant consequences of bottleneck effects in more recent periods suggest a recovery within these populations. Recovery means that there is a strong genetic background, which can carry considerable genetic diversity, necessary for the long-term survival and ability of these species to persist. In summary, the results emphasize the importance of preserving landscape connectivity and using conservation approaches that prioritize habitat restoration and wildlife corridors. These actions are necessary to ensure the survival of these species amidst continuous environmental alterations and anthropogenic pressures. Effective conservation strategies must therefore reduce fragmentation effects, facilitate gene flow, and ensure the long-term sustainability of populations of black bears and leopards in the Himalayan landscape. The results provide valuable information for the conservation of these species and set a foundation for future landscape-level genetic studies in the region. Declarations Author Contribution Authors and contribution: S.A.D, B.D.J, and L.K.S., Conceptualized. 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Genetics , 163(3), 1177–1191. Wright, S. (1949). The genetical structure of populations. Annals of Eugenics , 15(4), 323–354. Wright, S. (1984). Evolution and the genetics of populations. Experimental results and evolutionary deductions. University of Chicago Press, Chicago, USA. Wilson, G.A. & Rannala, B. (2003). Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163 , 1177–1191. Supplementary Material Supplementary Tables 1 and 2, and Figure S7, are not available with this version. Additional Declarations No competing interests reported. Supplementary Files SupplimentaryinformationfileGeneticspaperBlackbearandleopard.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 31 May, 2025 Reviewers agreed at journal 30 May, 2025 Reviewers agreed at journal 30 May, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 25 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 25 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6526590","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464527602,"identity":"a55b4217-8935-40f0-a2c2-06a798bb8ed1","order_by":0,"name":"Shahid Ahmad Dar","email":"","orcid":"","institution":"Zoological Survey of India","correspondingAuthor":false,"prefix":"","firstName":"Shahid","middleName":"Ahmad","lastName":"Dar","suffix":""},{"id":464527603,"identity":"acea80ff-c96b-4b78-a8ae-b8371151768d","order_by":1,"name":"Vinay Kumar Singh","email":"","orcid":"","institution":"Zoological Survey of India","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"Kumar","lastName":"Singh","suffix":""},{"id":464527604,"identity":"a47a8736-86b1-4eaa-b98e-39f870797466","order_by":2,"name":"Avijit Ghosh","email":"","orcid":"","institution":"Zoological Survey of India","correspondingAuthor":false,"prefix":"","firstName":"Avijit","middleName":"","lastName":"Ghosh","suffix":""},{"id":464527605,"identity":"bca4e9b1-fe75-4790-8226-3f3fe7731c32","order_by":3,"name":"Vineet Kumar","email":"","orcid":"","institution":"Zoological Survey of India","correspondingAuthor":false,"prefix":"","firstName":"Vineet","middleName":"","lastName":"Kumar","suffix":""},{"id":464527606,"identity":"d1ce60ae-5071-4786-8216-8a155d605d07","order_by":4,"name":"Hemant Singh","email":"","orcid":"","institution":"Zoological Survey of 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India","correspondingAuthor":false,"prefix":"","firstName":"Dhriti","middleName":"","lastName":"Banerjee","suffix":""},{"id":464527613,"identity":"a82677b0-b3e2-4099-b787-e01b573fbb3e","order_by":11,"name":"Lalit Kumar Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACPgbmhgMQJjOIlpAhqIWNgbHhAEQPWwJICw9RWhggWngMwCRhLewHGw9/+GWXJ+9+5vOrGzUWPAzsh49uwKuFJ7HhwMG+5GLDM7nbrHOOAR3Gk5Z2A7/DQFp6mBM3NuRuM85hA2qR4DHDr4X/IUhLfeLG/jfPjHP+EaNFAmjLgR+HE+dL5DA/zm0jSgvQlrMNxxM3SDwzY87tk+BhI+QXfv7kwx8q/lQnzu9Pfvw551udHD/74WN4tYABYxsDg8EBoI1gewkqB4M/DAzyDQzMH4hTPQpGwSgYBSMNAAAnUlAl2IxXAgAAAABJRU5ErkJggg==","orcid":"","institution":"Zoological Survey of India","correspondingAuthor":true,"prefix":"","firstName":"Lalit","middleName":"Kumar","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-04-25 07:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6526590/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6526590/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83810437,"identity":"5422a4d6-dc78-4e94-a1b1-45d8fca6fcad","added_by":"auto","created_at":"2025-06-03 06:53:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3366261,"visible":true,"origin":"","legend":"\u003cp\u003eMap of our study landscape, and unique genotype locations of Himalayan black bear and leopard.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6526590/v1/636665d33e1c64c8c54cb074.png"},{"id":83810403,"identity":"64506ccc-45e9-45ff-bc9b-226d017807a7","added_by":"auto","created_at":"2025-06-03 06:53:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1772290,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation genetic structure of Himalayan black bear. a) Results from STRUCTURE analysis at K5; b) Clustering results from DAPC, Individual colors and inertia ellipses depict population clusters, highlighting the genetic relationships and overlap among populations.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6526590/v1/c5eaf0750904adca2cf9a55f.png"},{"id":83810404,"identity":"018e3734-513f-49b3-bfbf-61f429734480","added_by":"auto","created_at":"2025-06-03 06:53:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1910314,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation genetic structure of leopard. a) Results from STRUCTURE analysis at K3; b) Clustering results from DAPC, Individual colors and inertia ellipses depict population clusters, highlighting the genetic relationships and overlap among populations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6526590/v1/87d5aaf43131593548bea85c.png"},{"id":83810405,"identity":"4a390f51-dde0-488d-8840-b086ab6a7f48","added_by":"auto","created_at":"2025-06-03 06:53:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":324764,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial autocorrelation of black bear and leopard. The \u003cem\u003eX\u003c/em\u003e-axis represents distance classes in km. The \u003cem\u003eY\u003c/em\u003e-axis is the coefficient for the correlation between genetic distance (Dps) and geographic distance for samples falling within each distance class. The dotted red lines represent the 95% confidence intervals.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6526590/v1/f661de3bf9bf850788ffc4a8.png"},{"id":83812164,"identity":"a1720e32-1dd3-417e-ae1b-c071fd7bf2f4","added_by":"auto","created_at":"2025-06-03 07:09:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12254054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6526590/v1/7e3971a8-1724-462f-97e3-ad9bafd2b09f.pdf"},{"id":83810408,"identity":"f3db3fc0-6a94-45bb-8deb-2b9fe6a190bf","added_by":"auto","created_at":"2025-06-03 06:53:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":52362089,"visible":true,"origin":"","legend":"","description":"","filename":"SupplimentaryinformationfileGeneticspaperBlackbearandleopard.docx","url":"https://assets-eu.researchsquare.com/files/rs-6526590/v1/170406615aa1ea64bf2a3120.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Asymmetric gene flow and genetic admixture underscore the importance of landscape connectivity in himalayan black bears and leopards","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHistorically, human land use change has led to the reduction and fragmentation of wildlife habitats globally and had a substantial impact on several wildlife populations (Fischer \u0026amp; Lindenmayer, 2007; Saunders et al., 1991). Anthropogenic habitat loss and fragmentation followed by climate change are known to be significant threats to wildlife across the globe and impact the genetic diversity and population genetic structure of wildlife populations by causing the subdivision and isolation of large and contiguous populations (Frankham et al., 2002; M\u0026eacute;ndez et al., 2011). The combined effect of reduced gene flow among the subgroups, inbreeding and stronger genetic drift leads to reduced genetic diversity, and species fitness, restricted dispersal ability and increased risk of local extinction (Wright 1949; Slatkin 1977; Frankham et al., 2002; Spielman et al. 2004; Hogg et al., 2006; Ripple et al., 2014). Consequently, such isolated populations with low levels of genetic diversity are less able to adapt to adverse environmental changes in the era of increased anthropogenic activities and climate change (B\u0026uuml;rger and Lynch 1995; Keller and Waller 2002; Bijlsma and Loeschcke 2012). Himalayan ecosystems possess complex topography, a wide range of habitat and environment gradients supporting several conservation important species which make this landscape sensitive to climate change and habitat fragmentation. This region is also important in terms of elusive species diversity yet least explored to understand the distribution, habitat ecology, genetic diversity and population genetic structure to inform effective conservation and management (Joshi et al. 2022; Joshi et al. 2020; Dahal et al. 2023). In addition, landscape-level population genetic studies of mammals are lacking from the Himalayas to inform the effective population monitoring for long-term species survivability, mitigate human-wildlife conflict and suggest management recommendations (Joshi et al. 2022; Kumar et al. 2022).\u003c/p\u003e \u003cp\u003eAmong mammals, large carnivores are particularly prone to these effects due to their extensive ranges in the Himalayas, low population densities, and long generation times (Noss et al., 1996). Further in the past two decades\u0026rsquo; bear-human and leopard\u0026ndash;human conflict drastically increased in the Himalayan states considering multiple factors demand to understand the movement of species, population growth trends and genetic diversity (Kumar et al. 2022; Naha et al. 2020). Despite their long-dispersal abilities, human development disrupts the gene flow of several large carnivore populations across the globe such as tigers and leopards in India (Thatte et al., 2020), pumas and American black bears in North America (Cushman and Lewis 2010; Ernest et al. 2014), Eurasian lynx in Europe (Bull et al. 2016), and jaguars in Mexico and Brazil (Roques et al., 2016). As a result, many large carnivore populations have become functionally isolated, leading to rapid declines in population size and increased inbreeding, with heightened extinction risks (Frankham et al., 2002; Crooks, 2002; Cushman \u0026amp; Lewis, 2010; Ernest et al., 2014; Thatte et al., 2020).\u003c/p\u003e \u003cp\u003eFurther, the Himalayas are sensitive to climate change and past studies have shown how past climatic events impact the population structure of species, and thus it is imperative to forecast how future climatic shifts will affect the genetic structure of species for effective conservation planning (Carnaval \u0026amp; Bates, 2007; Jay et al., 2012; Lima et al., 2017). The genetic variation within a species reflects the effects of gene flow, genetic drift and natural selection influenced by geographic distance and environmental factors. Therefore, it is essential to understand the genetic variation to reveal the demographic history and population structure (Lee \u0026amp; Mitchell-Olds, 2011; Eckert et al., 2008; Sork, 2016). Considering the complex topography wide elevation range provide wide environment gradients and along with geographic distance this could be one of the key factors contributing to genetic variation within the species, and likely affect the distribution of allele frequencies, gene flow and genetic structure in populations (Sork, 2016; Wang, 2013; Smith et al., 1997; Sacks et al., 2004; Freedman et al., 2010; Freeland \u0026amp; Kloepper, 2010; Thomassen et al., 2010). Therefore, for effective long-term conservation of species, it is essential to understand the role of environmental factors in shaping the gene flow and genetic structures, how habitat fragmentation and anthropogenic activities restrict the species movement as well as to disentangle their importance to geographic distance and barriers (Pease et al., 2009; Freedman et al., 2010; Manel et al., 2010; Sork et al., 2010). Among the carnivores, Himalayan black bear distribution is restricted to Himalayan states, while the leopard is found in a much wider range of habitats, extending across diverse habitats, from the foothills of the Himalayas to other parts of the Indian subcontinent. Himalayan black bears primarily prefer mixed broadleaf and coniferous forests, as well as alpine scrub, with a preference for elevations between 1200 to 3300 meters (Sathyakumar, 2001; Aryal et al., 2012). Similarly, leopards are highly adaptable carnivores inhabiting in a wide range of habitats, from tropical forests and dry scrublands to temperate forests and sub-alpine zones, usually up to 3000\u0026ndash;3500 meters (Lovari et al., 2013; Stein et al., 2020). Over the last few decades, anthropogenic pressures like poaching, habitat degradation, fragmentation as well as escalating human-wildlife conflict have resulted in population declines and range contractions for both species (Ripple et al., 2014). Therefore, conservation efforts focussing on maintaining landscape connectivity, mitigating conflict and reducing anthropogenic pressures are critical for ensuring the long-term survival of these species in the Himalayan region.\u003c/p\u003e \u003cp\u003eGiven that large carnivores require extensive, continuous habitats for survival, despite their high dispersal ability, gene flow is often negated by human disturbances (Crooks, 2002). Habitat loss, fragmentation and hunting driven by humans and climate change have led to significant population declines in these species over the past century (Nowell \u0026amp; Jackson, 1996; Perez, 2001). However, genetic studies of these species are limited, often due to challenges of obtaining the samples, leaving the research on their genetic patterns and response to environmental and human-related landscape disturbances poorly understood in many regions globally. In this study, we aim to fill this knowledge gap by examining two large carnivores\u0026rsquo; leopards and Himalayan black bears in the fragile Himalayan region, where human-induced and climate-driven changes have dramatic impacts. These species were chosen for their contrasting ecological requirements, leopards are highly adaptable to a wide range of habitats including disturbed areas (Balme et al., 2013; Jacobson et al., 2016). In contrast, Himalayan black bears are primarily forest dwellers, relying on seasonal food availability, making them more sensitive to habitat fragmentation (Cushman and Lewis, 2010). These ecological differences make them ideal species for understanding how environmental factors and human and climate-induced disturbances shape the genetic patterns and gene flow in large carnivores of this region. The key to biodiversity conservation lies in understanding the ecological web, this study aims to contribute to that understanding by studying two key players in the Himalayan region\u0026rsquo;s biodiversity.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eHimachal Pradesh, lies between 30\u0026deg;22' N and 33\u0026deg;12' N and 75\u0026deg;47' E and 79\u0026deg;04' E, and is a part of the ecologically significant and fragile Western Himalayan region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The region is ecologically rich and shows a great variation in elevation, ranges from 350 to over 6,000 m. The climate of this region varies significantly with elevation, from subtropical to alpine regions, heavily impacted by extreme temperatures and monsoonal rains (Sharma \u0026amp; Chettri, 2005). This topographical and climatic variation fosters a wide variety of forest types, including subtropical, temperate, and alpine forests, and likely provides homes to a wide range of wildlife species, including leopards, black bears, snow leopards, and numerous ungulate species (Saberwal, 1996; Negi, 1990). The interaction of topography, climate and habitat fragmentation in this region makes it an ideal place for studying how these climatic factors shape the genetic patterns and gene flow in species with discrete ecological requirements (Thakur et al., 2020; Sathyakumar, 2001). This study is particularly important for comprehending the broader effects of environmental factors on wildlife populations and for establishing efficient conservation initiatives.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design, Sample collection and DNA extraction\u003c/h3\u003e\n\u003cp\u003eWe used a grid-based sampling strategy to collect the non-invasive (scats) genetic samples of the study species (i.e., Common leopard and Himalayan black bear) across the study region. We first divided the study area into 5 \u0026times; 5 km sampling units (grid cells) based on the home range size of the study species. Then we selected the sampling grids based on the forest cover of the study landscape, likely the potential habitats of the study species. A total of 1514 grids were selected in different forest ranges of Himachal Pradesh both inside and outside protected areas. Then in each sampling grid, non-invasive genetic samples were collected opportunistically as well as systematically along animal and manmade trails (n\u0026thinsp;=\u0026thinsp;3, with a maximum length of 3 km) in the study area. All the field activities were conducted in collaboration with the forest department. Altogether 700 samples of black bear and 1200 samples of common leopard were collected across the study region. Then genomic DNA was extracted from the suitable samples using the Qiagen Kit method. Following DNA extraction, the samples were screened for species identification using species-specific Mitochondrial control region (For black bears: Taberlet and Bouvet 1994; and leopard: Hyun et al., 2020).\u003c/p\u003e\n\u003ch3\u003eMicrosatellite selection, screening and genotyping\u003c/h3\u003e\n\u003cp\u003eTo assess the population genetic patterns of our study species, 20 and 15 highly polymorphic microsatellite markers were screened for Himalayan black bears and leopards, respectively following the necessary criteria suggested by several non-invasive genetic studies (Waits et al. 2001; Bellemain and Taberlet, 2004). These markers were widely used globally in black bear and leopard genetic studies. However, based on PCR amplification success rate, 12 and 10 loci were selected to genotype all the field-collected samples of Asiatic black bear and leopard, respectively. Multiplex panels were designed to include the full set of loci for each species. The description of all the microsatellite markers used for black bear and leopard are given in supplementary Tables S1 and S2, respectively.\u003c/p\u003e \u003cp\u003eThe PCR reaction of each multiplex was setup with a 10 \u0026micro;l reaction volume containing 5 \u0026micro;l of 2X Multiplex MasterMix with HotStart Taq Polymerase (Qiagen), 2 \u0026micro;m BSA, 0.2 \u0026micro;m, 1 \u0026micro;l of forward fluorescence labelled primer (10\u0026micro;M) cocktail and 2 \u0026micro;l of extracted DNA. The PCR conditions were as follows: initial denaturation (95\u0026deg;C for 15 min), 40 cycles of denaturation (94\u0026deg;C for 35 s), annealing (varied between 50\u0026deg;C \u0026ndash; 62\u0026deg;C for 1 min) and extension (72\u0026deg;C for 90 s) and a final extension (70\u0026deg;C for 30 min). All the PCR products were genotyped using capillary electrophoresis on an ABI 3500XL sequencer and GeneScan\u0026ndash;500 LIZ\u0026reg; as the Size Standard (Applied Biosystems, Carlsbad, California). The alleles were scored using the program GeneMapper 3.7 (Applied Biosystems), and validated through visual inspection.\u003c/p\u003e\n\u003ch3\u003eGenotyping error rates, data validation and genetic variation\u003c/h3\u003e\n\u003cp\u003eTo ensure genotyping accuracy, each sample was genotyped thrice following a multi-tube approach (Miquel et al., 2006), and accepted the heterozygote only if we encountered two alleles in two attempts. The maximum likelihood of allele dropout (ADO) and false allele (FA) error rates were calculated using PEDANT v1.0, with 10,000 search steps to enumerate each error rate (Johnson and Haydon, 2007). The frequency of null alleles was estimated using the program FreeNa (Chapuis and Estoup, 2007). The scoring errors in the data were assessed and validated in MICROCHECKER 2.2.2 (Van Oosterhout et al., 2004).\u003c/p\u003e \u003cp\u003eThe cumulative PID (probability of identity) and PID (sibs) were calculated using GenAlEx v6.0 (Peakall and Smouse, 2012) to measure the power of a selected panel of markers to distinguish individuals. The summary statistics (i.e., number of alleles per locus, observed heterozygosity, expected heterozygosity, and inbreeding coefficient) were estimated using GenAlEx v6.0 (Peakall and Smouse, 2012). Tests for deviation from Hardy-Weinberg Equilibrium (HWE) and linkage disequilibrium for each locus were calculated using GenAlEx v6.0 (Peakall and Smouse, 2012).\u003c/p\u003e\n\u003ch3\u003ePopulation structure genetic structure\u003c/h3\u003e\n\u003cp\u003eIndividual-based Bayesian approach implemented in program STRUCTURE 2.3.3 (Pritchard et al., 2000) was used to infer the population genetic structure and contemporary genetic processes in black bear and leopard populations across the study landscape. STRUCTURE is a non-spatial model-based Bayesian clustering approach and infers the population structure based on allele frequency at each locus in a sample. The analysis was performed for K\u0026thinsp;=\u0026thinsp;2 to K\u0026thinsp;=\u0026thinsp;10, with 20 replicates for each K. The STRUCTURE runs were performed using the admixture model and correlated allele frequencies with 10\u003csup\u003e5\u003c/sup\u003e burn-in and 10\u003csup\u003e6\u003c/sup\u003e Markov chain Monte Carlo iterations (MCMC), and sampling location as a priori. The optimum number of biological populations was inferred by using likelihood distribution \u003cem\u003eL\u003c/em\u003e(\u003cem\u003eK\u003c/em\u003e) and the delta \u003cem\u003eK\u003c/em\u003e (Evanno et al., 2005), with the web version of Structure Harvester 5 v0.56.1 (Earl 2012). The assignment plot of STRUCTURE output was prepared using the program, DISTRUCT (Rosenberg 2004).\u003c/p\u003e \u003cp\u003eAdditionally, we carried out a Discriminant Analysis of the Principal Component (DAPC-Jombart et al., 2010) implemented in the R package adegenet (Jombart, 2008), to identify the clusters of genetically related individuals and the extent of allele sharing. DAPC is a multivariate model-free approach and is able to identify genetic clusters without any underlying population genetic assumptions, in particular Hardy-Weinberg equilibrium. DAPC explains the genetic variation by transforming the allelic data into uncorrelated components (PCA), and this makes DAPC more effective at identifying some complex spatial and hierarchical genetic structures (Evanno et al., 2005; Jombart et al., 2010; Kanno et al., 2011). Principal components (PCs) explaining\u0026thinsp;\u0026gt;\u0026thinsp;90% of variance were identified to determine the maximum variation between clusters, obtained through 10\u003csup\u003e6\u003c/sup\u003e MCMC iterations. The number of PCs retained was calculated using a cross-validation method with 100 replicates, as implemented in the \u003cem\u003e\u0026lsquo;xvalDapc\u0026rsquo;\u003c/em\u003e function (Jombart and Collins, 2015).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEffective population size and population bottleneck\u003c/h2\u003e \u003cp\u003eTo check the genetic bottlenecks for the whole population of study species, two qualitative approaches were used viz. (a) the Garza-Williamson index (or \u003cem\u003eM\u003c/em\u003e ratio) implemented in Arlequin v3.1 (Excoffier et al., 2005) and (b) the heterozygosity excess method implemented in Bottleneck v1.2.02 (Piry et al., 1999). The HET approach tests for heterozygote excess as compared with that expected under mutation\u0026thinsp;\u0026minus;\u0026thinsp;drift equilibrium. The significance of heterozygosity excess was determined by a one-tailed Wilcoxon test under three mutation models: Infinite Allele Model (IAM), Stepwise Mutation Model (SMM) or Two-phase Model (TPM). In TPM, 95% of single-step mutational steps (variance at 12%), with 10\u003csup\u003e4\u003c/sup\u003e iterations were used (Piry et al. 1999). Contemporary effective population size (\u003cem\u003eNe\u003c/em\u003e) was calculated using the linkage disequilibrium (Hill, 1981; Waples, 2006; Waples and Do, 2010) and molecular coancestry (Nomura 2008) methods implemented in the program NeESTIMATOR v.2.1 (Do et al. 2014). These two methods were used, as Wang et al. 2016 suggested that \u003cem\u003eNe\u003c/em\u003e estimates are sensitive to assumptions of different models. Given, that the \u003cem\u003eNe\u003c/em\u003e estimate characterizes the actual number of breeders in the entire population, but the estimates given by the above-mentioned methods reflect the effective number of breeders \u003cem\u003eNeb\u003c/em\u003e, which do not always accurately resemble the \u003cem\u003eNe\u003c/em\u003e (Nomura 2008). In the linkage disequilibrium model, the rare alleles seem to bias the results, therefore rare alleles with frequency less than 2% were excluded (\u003cem\u003ePCrit\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02; see Waples and Do, 2010).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene flow and migration rate\u003c/h3\u003e\n\u003cp\u003eThe pairwise estimates of F\u003csub\u003est\u003c/sub\u003e (Weir and Cockerham, 1984) was used as an indirect measure to determine the historical gene flow between populations of black bears and leopards. In addition, the rate of gene flow across the populations was expressed as the number of migrants per generation (Nm), where N is the effective population size and m is the proportion of migration per generation. Nm is calculated by (1/Fst-1)/4 (Wright 1984; Slatkin 1987). BayesAss v3.0 was used to calculate the recent migration rate (m - last 5\u0026ndash;6 generations) between the populations of study species (Wilson and Rannala, 2003). Multiple simulations (n\u0026thinsp;=\u0026thinsp;3) were carried out with different seed numbers using 10\u003csup\u003e7\u003c/sup\u003e MCMC iterations, of which 106 were discarded as burn-in periods. In addition, \u0026lsquo;detect migrants\u0026rsquo; implemented in GENECLASS 2.0 was used to identify the first-generation migrants (i.e., individuals born in a population other than the one in which they were sampled). A Bayesian approach (Rannala and Mountain 1997) and the resampling method of Paetkau et al. (2004) were used with 10,000 simulated individuals at an alpha of 0.01. A likelihood ratio test was calculated and used to compare the population where the individual was sampled over the highest value among all populations (\u003cem\u003eL\u0026thinsp;=\u0026thinsp;L_home/L_max\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eTo further investigate the contemporary migratory patterns and directional relative magnitude between landscape populations, migration networks were generated using the \u003cem\u003eDivMigrate\u003c/em\u003e function (Sundqvist et al., 2016) within the diveRsity R package (Keenan et al., 2013). The relative migration rates were calculated using G\u003csub\u003eST\u003c/sub\u003e (Nei, 1973) and Jost D (Jost, 2008) as a measure of genetic distance with 1000 bootstrap repetitions.\u003c/p\u003e\n\u003ch3\u003eIsolation of distance (IBD)\u003c/h3\u003e\n\u003cp\u003eThe patterns of gene flow between landscape regions for black bear and leopard populations were evaluated under the hypothesis of isolation by distance (IBD), assuming that black bear and leopard movement decisions in the study landscape are affected purely by geographic distance and hypothesize that genetic exchange occurs more between neighbouring individuals than distant Individuals. To assess the effect of isolation by distance on the genetic divergence of black bear and leopard, pairwise euclidean distance and genetic distance between each individual were calculated. The euclidean distance was calculated using a uniform raster with all grid cell values equal to 1 using the \u003cem\u003eadegenet\u003c/em\u003e package in R (Jombart 2008). To calculate the genetic distance, we used the propShared function in the \u003cem\u003eadegenet\u003c/em\u003e package of R (Jombart, 2008), and calculated the proportion of shared alleles (Dps) between each individual of the study species. Dps is a measure of similarity, whereas genetic distance is a measure of dissimilarity, therefore Dps and genetic distance are inversely related. Then IBD was determined through the correlation between the genetic distance (Dps) and the geographic distance (Euclidean) and was assessed using Mantel\u0026rsquo;s test in the adegenet package in R (Jombart, 2008). Pairwise spatial autocorrelation was carried out with GENALEX version 6.5 (Peakall \u0026amp; Smouse, 2012), and was compared across the scale of distance classes (5-300 km for both black bears and leopards). Correlograms of the correlation coefficients (r) for each distance class were used for visualization. The statistical significance through statistical processing of the null hypothesis of r\u0026thinsp;=\u0026thinsp;0, 95% confidence intervals were generated for each distance class using 1000 simulations and 99 bootstrap repeats (Thatte et al., 2019). This method helped to explain the spatial patterns of genetic structure and the possible role of geographic distance in the genetic variation of the species.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpatial patterns of effective population size and genetic diversity\u003c/h2\u003e \u003cp\u003eTo assess the spatial patterns of effective population size (NS) and genetic diversity under IBD, the standard diversity indices and NS were calculated based on the Wright-Fisher population and Wright\u0026rsquo;s genetic neighbourhood concept (Shirk and Cushman, 2011; Shirk and Cushman, 2014). The genetic neighbourhood radius was determined by the mean squared average parent-offspring dispersal distance. Then, moving window analysis was conducted using a neighbourhood radius of 20 km to map the NS and genetic diversity spatially.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping error rates\u003c/h2\u003e \u003cp\u003eThe genotyping error rates of microsatellite loci used for leopard and black bear are summarized in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively. The genotyping error rate varied among the loci used for leopard, with an average allelic dropout (ADO) rate and false allele (FA) of 0.20 and 0.03, respectively. The null allele frequency ranged between 0.15 and 0.29 with an average of 0.24 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the genotyping error rate varied among the loci used for Asiatic black bears, with an average allelic dropout (ADO) rate and false allele (FA) of 0.04 and 0.01, respectively. The null allele frequency ranged between \u0026minus;\u0026thinsp;0.05 and \u0026minus;\u0026thinsp;0.22 with an average of -0.09 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eGenotyping error rates and genetic characterisation of Asiatic black bear at twelve microsatellite loci in Himachal Pradesh.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFis (W\u0026amp;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eADO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF(null)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP\u003csub\u003eID\u003c/sub\u003e (Cum)\u003c/p\u003e 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\u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.1E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSUT8*\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.1E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUT1*\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.3E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUT29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.6E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSUT7*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSUT1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.2E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0000021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSE (\u0026plusmn;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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 \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\u003eGenotyping error rates and genetic characterisation of leopard at ten microsatellite loci in Himachal Pradesh.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFis (W\u0026amp;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eADO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF(null)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP\u003csub\u003eID\u003c/sub\u003e (Cum)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP\u003csub\u003eID\u003c/sub\u003e sib (Cum)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFCA272*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.7E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.2E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePUN327*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.1E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.1E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFCA090*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.7E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.8E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFCA043*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.7E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.4E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePUN132*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.4E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.4E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFCA232*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.1E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.1E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFCA672*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.3E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.0E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePUN124\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFCA628\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePUN80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenetic variation\u003c/h2\u003e \u003cp\u003eFor black bears, the cumulative probability of identity (PID biased) value of the microsatellite marker panel was 9.2 \u0026times; 10\u003csup\u003e\u0026ndash;19\u003c/sup\u003e, and the probability of identity (PID sibs) was 2.1 \u0026times; 10\u003csup\u003e\u0026ndash;6\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, for the leopard, the cumulative probability of identity (PID biased) value of the microsatellite marker panel was 5.3 \u0026times; 10\u003csup\u003e\u0026ndash;09\u003c/sup\u003e, and the probability of identity (PID sibs) was 2.1 \u0026times; 10\u003csup\u003e\u0026ndash;6\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 307 and 333 unique genotypes of black bear and common leopard, respectively were identified across the study region.\u003c/p\u003e \u003cp\u003eAll the 12 microsatellite markers used for black bears were polymorphic, and the number of alleles at each locus ranged from 9 to 19, with a total of 158 alleles (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The global mean observed heterozygosity (Hobs) and expected heterozygosity (Hexp) were 0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 and 0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 across 12 polymorphic loci, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, all 10 markers used for leopard were polymorphic, and the number of alleles at each locus ranged from 5 to 14, with a total of 103 alleles (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The global mean observed heterozygosity (Hobs) and expected heterozygosity (Hexp) were 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 and 0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 across 10 polymorphic loci, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure\u003c/h2\u003e \u003cp\u003eThe results based on the Bayesian approach implemented in STRUCTURE\u003c/p\u003e \u003cp\u003eshowed the maximum differentiation at K\u0026thinsp;=\u0026thinsp;5 clusters for black bears and K\u0026thinsp;=\u0026thinsp;3 clusters for leopards, as inferred by lnP(K) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For black bears, the Chamba and Kangra populations showed a clear genetic distinctiveness with minimal sharing from the specific regions while other regions exhibit a high level of admixture, reflecting genetic contributions from diverse regions. For leopards, all populations display varying degrees of admixture, reflecting genetic contributions from multiple clusters. This pattern suggests a substantial level of genetic exchange and gene flow among the leopards across the different regions within the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo examine the genetic clustering using the multivariate approach, specifically discriminate analysis of principal component (DAPC), the number of PCs to be retained from \u003cem\u003eoptim.a.score\u003c/em\u003e function varied between 24 and 30 for black bears and 15 to 20 for leopards, while the cross-validation technique with 100 replicates estimated optimal PCs to be 30 for black bears and 20 for leopards. Therefore, 30 PCs were retained for the black bear and 20 for the leopard for the final analysis. The DAPC results showed a varied level of overlapping pattern with no clear clustering among the different regions within the study landscape for both the study species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting considerable genetic exchange within both species across the study region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffective population size and population bottleneck\u003c/h2\u003e \u003cp\u003eThe M-ratio (G-W index) calculated across polymorphic loci was below the threshold of 0.68, suggesting evidence of a historical genetic bottleneck in both black bear and leopard populations across the study region (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the heterozygosity tests (HET), conducted under the stepwise mutation model, did not detect any significant signs of recent genetic bottlenecks in both the study species. Additionally, a normal L-shaped allelic distribution was observed in both species, which is typically characteristic of populations that may not have experienced recent bottlenecks.\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\u003eSummary of bottleneck analyses and effective population sizes (NE) of Himalayan black bear and leopard in Himachal Pradesh.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutation model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWilcoxon test\u003c/p\u003e \u003cp\u003e(H-excess)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllele frequency distribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM-ratio\u003c/p\u003e \u003cp\u003e(G-W index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNE (CI95%) (Pcrit\u0026thinsp;=\u0026thinsp;0.02)\u003c/p\u003e \u003cp\u003eLD method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHimalayan black bear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eL-Shaped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e204.5\u003c/p\u003e \u003cp\u003e(182.9\u0026ndash;230.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.2348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCommon leopard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eL-Shaped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.52\u0026thinsp;+\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e208.1\u003c/p\u003e \u003cp\u003e(175.4\u0026ndash;251.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.6152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eIAM\u0026thinsp;=\u0026thinsp;infinite alleles model, TPM\u0026thinsp;=\u0026thinsp;two phase mutation model, SMM\u0026thinsp;=\u0026thinsp;stepwise mutation model and #significant p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe estimates of contemporary effective population size, derived using linkage disequilibrium (LD) revealed high effective population size values for both species in this region. For black bears, the overall effective population size was estimated at 204.5 (95% CI: 182.9\u0026ndash;230.5), while for leopards the effective population size was estimated at 208.1 (95% CI: 175.4\u0026ndash;251.4) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results suggest that, despite the historical bottlenecks, the current effective population sizes for both species are relatively stable reflecting the recovery population dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGene flow and migration rate\u003c/h2\u003e \u003cp\u003eLow to moderate genetic distance measure (F\u003csub\u003eST\u003c/sub\u003e, Table S3) was observed between the landscape regions for Himalayan black bear and leopard, therefore suggesting some gene flow between these landscapes. The recent migration analysis using BayesAss resulted in asymmetric gene flow in both the study species within the study region (Table S4, S5), with relatively higher gene flow within the region than between the regions. GENECLASS 2.0 identified a total of 18 individuals of black bears as first-generation migrants across the study landscape (Table S6). In contrast, only 6 individuals of leopards were identified as first-generation migrants across different regions within the study landscape (Table S7).\u003c/p\u003e \u003cp\u003eThe historical and contemporary directional migration rates per generation, estimated using the DivMigrate function within the \u003cem\u003ediveRsity\u003c/em\u003e R package, indicated predominantly asymmetric gene flow between landscape region pairs for both Himalayan black bears and leopards (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2, S3, S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of distance (IBD)\u003c/h2\u003e \u003cp\u003eSpatial autocorrelation analysis conducted for Himalayan black bear and leopard populations using Mantel's test revealed that there was no significant correlation between genetic distance and geographic distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Thus, the IBD hypothesis seems not to be valid for these species in the study area. Redundancy analysis (RDA) results demonstrated that 5.22% of the total genetic variation was explained by spatial variables for leopards, while 7.09% of genetic variation was explained for black bears in this region (Figure S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSpatial patterns of effective population size and genetic diversity\u003c/h2\u003e \u003cp\u003eThe results showed a substantial difference in spatial genetic patterns under the IBD model for both the study species based on the average dispersal distance used in the analysis (Figures S6, S7). Our results indicated that areas with low, moderate to high levels of NS and genetic diversity indices were patchily distributed across the study region for both black bears and leopards.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe genetics studies on Himalayan black bears and leopards have received less attention and remain relatively understudied in the Himalayan landscape compared to other carnivores across their global ranges. While numerous genetic studies have been carried out on black bears in North America (Onorato et al., 2004; Csiki et al., 2003), and leopards in Asia and Africa (Jacobson et al., 2016; Uphyrkina et al., 2001 Bhatt et al. 2020). No landscape-level genetic studies have been conducted to investigate the population genetics of these species in this region (Singh et al. 2021). Hence, for the first time, we attempted to evaluate the genetic composition of the two large carnivores at a fairly large landscape covering the entire State of Himachal Pradesh. The present study represents a foundational effort and an important attempt to fill this gap. Therefore, the findings of this study provide important insights toward knowing the genetic composition of these two species. Such information is essentially necessary for their effective long-term conservation planning, especially addressing the problems by human-induced habitat fragmentation in this region.\u003c/p\u003e \u003cp\u003eThe results revealed a moderate level of genetic diversity in both the Himalayan black bear and leopard populations in this region. For black bears, the global mean observed heterozygosity and expected heterozygosity were estimated at 0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 and 0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, respectively. Similarly, for leopards, the mean observed heterozygosity and expected heterozygosity were estimated at 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 and 0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, respectively. The observed and expected heterozygosity values are lower than those reported in other studies on these species in different landscapes in India, such as for black bears (Ho\u0026thinsp;=\u0026thinsp;0.58, Mukesh et al., 2015), and for leopards (Ho\u0026thinsp;=\u0026thinsp;0.57, Mondol et al., 2009; Ho\u0026thinsp;=\u0026thinsp;0.74, Dutta et al., 2013). Our findings on heterozygosity levels indicate moderate genetic variation compared to studies on these species globally. For example, the observed heterozygosity levels in North American black bears (Ho\u0026thinsp;=\u0026thinsp;0.68, Csiki et al., 2003) and leopards in Africa and Asia (Ho\u0026thinsp;=\u0026thinsp;0.57\u0026ndash;0.71, Uphyrkina et al., 2001; Jacobson et al., 2016) are generally higher, reflecting less fragmented and larger population sizes in those landscapes. Further, the comparatively lower genetic diversity in black bears and leopards may reflect both historical and present pressures, that range from habitat fragmentation to human-wildlife conflict and restricted dispersal opportunities within the Himalayan topography. Similar to the genetic diversity patterns previously reported for other carnivore populations across the globe, including Kodiak Island brown bear (Ho\u0026thinsp;=\u0026thinsp;0.26, Paetkau et al., 1998) and Gobi bear populations (Ho\u0026thinsp;=\u0026thinsp;0.51, Tumendemberel et al., 2019). Furthermore, the positive inbreeding coefficient observed in both the study species indicates that the populations of these species in HP are inbred, which can be the putative reason for the moderate level of genetic diversity among them.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePopulation genetic structure\u003c/h2\u003e \u003cp\u003eThe Bayesian clustering results indicated moderate genetic differentiation in leopards and black bears across the study landscape. Notably, the Chamba and the Kangra populations of black bears exhibited distinct genetic profiles, suggesting minimal gene flow between these areas and the surrounding regions. Contrastingly, the other regions displayed substantially high admixture, indicating extensive gene exchange and population movement (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the leopards, in general, the results by STRUCTURE revealed significant genetic intermixing admixture among all populations, indicating a high level of gene flow throughout the study area These results are consistent with previous studies showing the potential for gene flow in widely distributed species like leopards, by which they can sustain the genetic diversity even in fragmented landscapes (Jacobson et al., 2016; Uphyrkina et al., 2001).\u003c/p\u003e \u003cp\u003eThe Discriminant Analysis of Principal Components (DAPC) corroborated the results from STRUCTURE, showing substantial overlap and no clear population clustering, suggesting considerable genetic exchange across the study regions for both species. Such results indicate a dynamic population structure with little genetic isolation and support the idea of gene flow. The findings fit well with other large-home-range carnivores with wide dispersal capabilities, like the leopard and tiger, which can show little genetic differentiation even in the presence of habitat fragmentation (Gopal et al., 2012; Jhala et al., 2015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIsolation by distance\u003c/h2\u003e \u003cp\u003eWe did not find a significant relationship between geographic distance and the proportion of shared alleles in both study species, demonstrating the absence of isolation by distance. This implies that in our study region, the genetic divergence cannot be solely explained by geographic distance in these two study species and corroborated other studies in the Himalayan landscape (Dahal et al. 2023). The lack of significant IBD is particularly interesting, considering the ability of these species to traverse large distances. However, habitat fragmentation brought on by human-caused factors like road development, human encroachment and deforestation may be the possible cause of the observed genetic differentiation in these species in this region. Indeed, these findings align with previous research on other carnivores, which demonstrated that habitat fragmentation often plays a key role in genetic structuring rather than geographic distance (Mondol et al., 2013; Shrestha et al., 2020). Interestingly, despite genetic differentiation, the limited population structuring observed in our study indicates that certain landscape features may be mitigating the impacts of fragmentation. The Himalayan region, although impacted by human activities in recent decades, still has largely intact habitats that maintain a level of connectivity and permit movement and gene flow between populations. This supports the idea that mountainous areas\u0026rsquo; intact or semi-intact habitats can mitigate the consequences of habitat fragmentation (Schoville et al., 2012). On the other hand, if habitat fragmentation is certainly occurring, the timescale of such changes may not be long enough to leave observable genetic traces. Studies suggested that it can take multiple generations, often more than 50 years, for the genetic effects of habitat fragmentation to become evident (Epps \u0026amp; Keyghobadi, 2015; Landguth et al., 2010). Since genetic differentiation develops over an evolutionary timescale, our dataset may not fully reflect the impacts of recent anthropogenic fragmentation, underscoring the importance of continued monitoring of these species in the study region.\u003c/p\u003e \u003cp\u003eSuch observations accentuate the fundamental role landscape connectivity plays, which appeals to landscape connectivity strategies being the most suitable method for maintaining genetic diversity, particularly in those ecologically adaptable species, like a black bear or leopard that may occupy broad habitat ranges (Cushman et al., 2006; Proctor et al., 2015; Khosravi et al., 2018; Beier et al., 2008; Nawaz et al., 2014). Therefore, the conservation efforts in the region should be targeted towards wildlife corridors and habitat restoration that will probably minimize the impacts of habitat fragmentation on sustainable populations of species. Therefore, the conservation efforts in the region should be targeted towards wildlife corridors and habitat restoration that will probably minimize the impacts of habitat fragmentation on sustainable populations of species.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePopulation bottleneck and effective population size\u003c/h2\u003e \u003cp\u003eBottleneck analysis represented by the M-ratio index calculated over polymorphic loci indicated evidence of a historic genetic bottleneck in black bears and leopards in this region. This implies that the two species may have experienced population declines in the past, likely due to climatic events, habitat fragmentation or other anthropogenic pressures, as such bottlenecks are often linked with reduced genetic diversity. However, the absence of significant effects from the heterozygosity test under the stepwise mutation model and the fact that both species show a normal L-shaped allelic distribution indicate that the populations have probably recovered from these declines and are not currently experiencing a genetic bottleneck. These findings align with the hypothesis that populations that have recovered from past bottlenecks can eventually regain genetic diversity, especially in species with relatively high reproductive rates or when gene flow occurs between populations that have been fragmented (Frankham et al., 2002; Garcia-Dorado et al., 1999).\u003c/p\u003e \u003cp\u003eIn addition, the contemporary estimates of the effective population sizes, through linkage disequilibrium analysis, revealed that the Ne value for both species is pretty high: it is about 204.5 in black bears and 208.1 in leopards. This may suggest the absence of severe historical bottlenecks that could hamper the healthy levels of genetic diversity today. Such estimates are important for conservation strategies since they indicate a resilient population structure that can withstand the future challenges posed by climate change and habitat fragmentation (Charlesworth \u0026amp; Charlesworth, 2010).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides considerable insight into the population genetics of the Himalayan black bear and leopard, species whose genetic frameworks have received very limited investigation in the Himalayan ecosystem. Results: Both species showed a moderate level of genetic diversity; the levels of observed heterozygosity were lower than those reported in other populations of these species found in other parts of the world. This diminished genetic diversity is ascribed to historical and continuing pressures, including habitat fragmentation and conflicts between humans and wildlife, which restrict both gene flow and opportunities for dispersal. The analysis of the population genetic structure indicated an average genetic differentiation among landscapes under study, with considerable gene movement between regions, especially for the leopards. The STRUCTURE and DAPC analyses pointed out the admixtures of genetic and fluid population structures in the study regions that underlie gene flow despite fragmentation in their habitat. Still, the lack of noticeable isolation by distance (IBD) relationship suggests that it is not the geographic distance alone that explains genetic divergence among the species studied. Conversely, habitat fragmentation caused by such human activities as road building and deforestation can more dramatically explain species' genetic differences.\u003c/p\u003e \u003cp\u003eThe bottleneck phenomena analyzed indicated the presence of pre-existing genetic bottlenecks in both species, likely resulting from previous fragmentation events. However, the currently high effective population sizes observed in both black bears and leopards and the lack of significant consequences of bottleneck effects in more recent periods suggest a recovery within these populations. Recovery means that there is a strong genetic background, which can carry considerable genetic diversity, necessary for the long-term survival and ability of these species to persist. In summary, the results emphasize the importance of preserving landscape connectivity and using conservation approaches that prioritize habitat restoration and wildlife corridors. These actions are necessary to ensure the survival of these species amidst continuous environmental alterations and anthropogenic pressures. Effective conservation strategies must therefore reduce fragmentation effects, facilitate gene flow, and ensure the long-term sustainability of populations of black bears and leopards in the Himalayan landscape. The results provide valuable information for the conservation of these species and set a foundation for future landscape-level genetic studies in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors and contribution: S.A.D, B.D.J, and L.K.S., Conceptualized. S.A.D, V.K.S, A.G and B.D.J., performed the analysis. S.A.D., V.K, H.S, A.S, A.P.S, R.D., and B.D.J., carried out the field work. S.A.D, B.D.J and L.K.S prepared the first draft of the manuscript, S.A.D, V.K.S, A.G, V.K, H.S, R.D, A.S, A.P.S, and B.D.J curated data. All authors reviewed and finalised the manuscript. L.K.S. supervised the study and provided data and other resources for implementing the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, C. D., et al. (2010). Considering spatial and temporal scale in landscape-genetic studies of gene flow. Molecular Ecology, 19(17), 3565-3575.\u003c/li\u003e\n\u003cli\u003eAryal, A., Raubenheimer, D., \u0026amp; Ji, W. (2012). Distribution and diet of the Asiatic black bear (Ursus thibetanus) in the Himalayas, Nepal. Ursus, \u003cstrong\u003e23\u003c/strong\u003e(1), 31\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eBalme, G. A., Lindsey, P. A., Swanepoel, L. H., \u0026amp; Hunter, L. T. (2013). 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Bayesian inference of recent migration rates using multilocus genotypes. \u003cem\u003eGenetics \u003c/em\u003e\u003cstrong\u003e163\u003c/strong\u003e, 1177\u0026ndash;1191.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Material","content":"\u003cp\u003eSupplementary Tables 1 and 2, and Figure S7, are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Landcape connectivity, Gene Flow, Resilience, Himalayas, Carnivores, Himalayan Black Bear, Common Leopard","lastPublishedDoi":"10.21203/rs.3.rs-6526590/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6526590/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComprehending the genetic characterization of wildlife populations is fundamental for the formulation of effective conservation measures, especially in fragmented and fragile environments like the Himalayas. This study investigates the population genetics of Himalayan black bears (\u003cem\u003eUrsus thibetanus\u003c/em\u003e) and leopards (\u003cem\u003ePanthera pardus\u003c/em\u003e) in Himachal Pradesh, a Indian State experiencing escalated anthropogenic influences and large scale development. Using microsatellite markers and a combination of Bayesian clustering, discriminant analysis of principal components (DAPC), and spatial genetic analyses, we evaluated the patterns of genetic diversity, population structure, gene flow and evidence of isolation by distance (IBD) for these species. The results showed a relatively low to moderate level of genetic diversity, with Ho\u0026thinsp;=\u0026thinsp;0.35 for black bears and Ho\u0026thinsp;=\u0026thinsp;0.41 for leopards, and evidence of historical genetic bottlenecks but stable contemporary effective population sizes for both study species (204.5 for black bears, 208.1 for leopards). Furthermore, we found moderate genetic differentiation, high admixture and asymmetric gene flow across the study region for both the study species, with no significant isolation by distance (IBD). The findings of this investigation highlight the resilience of these species while emphasizing habitat connectivity as the critical factor for preserving genetic diversity. Implementation of conservation strategies such as wildlife corridors and habitat restoration to alleviate fragmentation and sustain populations over time is recommended. This research established a basis for subsequent studies focused on genetics and ecology, and improved the comprehensive understanding of wildlife conservation within the Himalayan ecosystem.\u003c/p\u003e","manuscriptTitle":"Asymmetric gene flow and genetic admixture underscore the importance of landscape connectivity in himalayan black bears and leopards","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 06:53:01","doi":"10.21203/rs.3.rs-6526590/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-03T11:37:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T10:42:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T21:11:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149741110279826180169985405677483220856","date":"2025-05-31T10:08:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56900172134567764213765552967644952268","date":"2025-05-30T13:17:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145628965587961380508565641075011158389","date":"2025-05-30T13:15:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-30T12:54:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-26T01:26:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T01:24:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2025-04-25T07:43:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a4fdff68-1f31-4141-85cc-3e1e2a40429d","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 06:53:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6526590","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6526590","identity":"rs-6526590","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}