Genetic Erosion at the Edge: Landscape Fragmentation and Connectivity in Sloth Bears of the Indian Terai

Genetic Erosion at the Edge: Landscape Fragmentation and Connectivity in Sloth Bears of the Indian Terai | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Genetic Erosion at the Edge: Landscape Fragmentation and Connectivity in Sloth Bears of the Indian Terai View ORCID Profile Divyashree Rana , Judi Clarissa Samjith , View ORCID Profile Uma Ramakrishnan doi: https://doi.org/10.1101/2025.09.27.678944 Divyashree Rana 1 National Centre for Biological Sciences, Tata Institute of Fundamental Research , Bangalore, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Divyashree Rana For correspondence: divyashreerana24{at}gmail.com Judi Clarissa Samjith 1 National Centre for Biological Sciences, Tata Institute of Fundamental Research , Bangalore, India 2 Department of Biotechnology, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education (MAHE) , Manipal, 576104, Karnataka, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Uma Ramakrishnan 1 National Centre for Biological Sciences, Tata Institute of Fundamental Research , Bangalore, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Uma Ramakrishnan Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Habitat fragmentation is a major global threat to biodiversity, particularly for species at the periphery of their range where populations are smaller and historically more isolated. Fragmented landscapes reduce connectivity, erode genetic diversity, and elevate inbreeding risk, potentially leading to local extinction. Landscape genetics offers a powerful framework to assess how land-use patterns influence gene flow and population viability. We investigated the endemic sloth bear ( Melursus ursinus ) populations in the Indian Central Terai landscape, a multi-use, high human density region exemplifying fragmentation and range-edge pressures. Using a ddRAD-seq based approach on non-invasively collected faecal samples (n=209), we generated genomic data, and identified 66 individuals using 964 SNP markers. Despite weak spatial genetic structure, with Dudhwa populations showing the highest differentiation (F ST = 0.30). The populations exhibited extremely low heterozygosity (H O = 0.073) and high fixation index (F IS = 0.45), indicating recent bottlenecks and isolation. Landscape genetic analyses revealed agriculture density as the primary barrier to gene flow, followed by road density. Compared to sloth bear populations in Central India and Nepal Terai, CTL bears exhibit markedly reduced genetic variation, likely driven by historic fragmentation and peripheral range effects. This study provides the first genomic baseline for sloth bears in the CTL and underscores the urgency of integrating genetic monitoring with habitat management to prevent further erosion of genetic diversity and ensure long-term population persistence. 1. Introduction Fragmentation of habitats not only reduces the overall availability of suitable area but also hinders animal movement, thereby impeding their access to mates and resources ( Francis et al., 2016 ). Predominantly driven by anthropogenic factors such as urbanisation, agricultural expansion and industrialisation, it is recognised as a major threat to biodiversity globally ( Fahrig, 2003 ). It creates isolated patches embedded within human-modified matrices that are largely unsuitable for wildlife, thereby limiting dispersal. The resulting loss of connectivity leads to small, isolated populations that exhibit increased inbreeding ( Frankham, 1998 ), reduced allelic richness ( Allendorf et al., 2024 ) and heightened demographic stochasticity ( Araya-Ajoy et al., 2025 ), ultimately lowering fitness and evolutionary potential ( Allendorf, 1986 ). These effects are particularly severe for species with limited dispersal abilities and large home ranges ( Cardillo et al., 2004 ). Conservation scenarios such as the inbreeding depression of the Florida panther prior to genetic rescue ( Johnson et al., 2010 ), the prolonged isolation and collapse of Isle Royale’s gray wolves ( Hedrick et al., 2014 ) and the unusually high frequency of a rare recessive trait in the melanistic tigers of Similipal Tiger Reserve ( Sagar et al., 2021 ) provide documented evidence of the genetic and demographic consequences faced by small, isolated populations. Collectively, these cases underscore the critical importance of maintaining habitat connectivity as a conservation priority to ensure long-term population viability. These concerns are further amplified among populations located at the periphery of a species’ geographic range as articulated by the ‘centre-marginal hypothesis’ ( Brown, 1984 ). The hypothesis suggests that populations at the range periphery often exhibit reduced genetic diversity and increased differentiation as compared to the central populations. This is often as a result of historical range expansions and founder events, leading to smaller, isolated populations that experience stronger drift and reduced gene flow ( Eckert et al., 2008 ). Though these effects are not universal ( Wall et al., 2025 ), many studies have documented reduced abundance, increased demographic instability and decreased genetic diversity exhibited by range-end populations ( Hasui et al., 2024 ; Sexton et al., 2009 ). Mostly tested on plants ( Abeli et al., 2014 ; Brzosko et al., 2009 ) and reptiles ( Gassert et al., 2013 ; O’Brien et al., 2015 ), evidence from mammals is limited ( Quemere et al., 2010 ; Arriagada et al., 2025 ), specifically for wide-ranging mammals. Therefore, inferring the genetic and ecological effects for range-edge populations is crucial, as these populations may experience a heightened vulnerability to anthropogenic or natural pressures. Given the conservation concerns associated with small and isolated populations, it is essential to understand the role of both natural and anthropogenic fragmentation in maintaining functionally connected populations. Assessing how landscape features influence genetic connectivity allows us to evaluate the extent to which physical and environmental barriers impede gene flow ( Manel et al., 2003 ). Identifying and prioritizing such areas is critical for effective conservation planning and long-term population viability. Recent advances in analytical approaches to optimize resistance surfaces and predict functional connectivity have improved our understanding of species-specific landscape permeability ( Peterman, 2018 ; Shirk et al., 2018 ). These tools have helped evaluate the effectiveness of the existing protected areas and corridors ( Naidoo et al., 2018 ) as well as prediction of how future land use changes may affect dispersal and movement ( van Strien et al., 2014 ). When integrated with empirical genetic data, such models can uncover cryptic isolation patterns ( Wasserman et al., 2012 ) and detect functional corridors (Sharma et al., 2013), offering particular value for wide-ranging and elusive taxa. Advancements in DNA extraction and sequencing technologies have expanded the potential of non-invasively collected faecal, hair or saliva to infer population-level genetic parameters, including structure, connectivity and diversity, without directly handling individuals ( Taberlet et al., 1999 ). This approach has been particularly valuable for elusive and endangered species but requires careful optimization to address the low quantity and degraded quality of DNA typically obtained ( Bourgeois et al., 2019 ). Traditionally, capillary-based fragment analysis of hypervariable microsatellites has been employed for such samples; however, these markers are limited by their number, prone to genotyping errors, and often suffer from low reproducibility ( Estoup et al., 2002 ; Sittenthaler et al., 2020 ). Amplicon-based sequencing using standardized marker panels that target short DNA fragments has emerged as a reproducible and more robust alternative ( De Barba et al., 2017 ; Rana et al., 2024 , Natesh et al., 2019 ). Beyond targeted panels, reduced-representation genomic approaches such as double-digest restriction site-associated DNA sequencing (ddRAD-seq) enable simultaneous discovery and genotyping of hundreds of SNPs from non-invasive samples ( Jones et al., 2025 ; Tyagi et al., 2022 , 2024 ). With the increasing accessibility of these genomic approaches, non-invasive sampling offers a powerful means to overcome the logistical challenges of direct monitoring and to generate robust insights into the genetic processes shaping populations in fragmented landscapes. The Central Terai Landscape (CTL) in India, an altered multiuse landscape characterized by high human density, anthropogenic pressures, and rich biodiversity, exemplifies the challenges of conserving wide-ranging species in shared spaces ( Thapa et al., 2021 ). The sloth bear (Melursus ursinus), a large carnivore within this landscape, is now largely restricted to protected areas. Its distribution in the CTL is highly fragmented, and at a broader scale this region represents the northern limit of the species’ range in India ( Dharaiya et al., 2017 ). With remnant habitats embedded within a matrix of agriculture and settlements, understanding the functional connectivity of sloth bear populations in this critical transboundary region is essential. To address this, we used a SNP-based approach applied to non-invasively collected faecal samples to investigate population structure and connectivity. Specifically, we aimed to (i) optimize a non-invasive genetic protocol for monitoring sloth bears, (ii) evaluate genetic diversity and structure across protected areas, and (iii) map functional connectivity across the CTL. Given the combined pressures of habitat fragmentation and range-edge effects, we hypothesize that sloth bear populations in the CTL will exhibit reduced genetic diversity and elevated genetic differentiation, reflecting the impact of landscape modification. 2. Methods 2.1. Study area, focal species, and sampling Non-invasive sampling was conducted across the core and buffer areas of Pilibhit Tiger Reserve (PBT), Dudhwa National Park (DDW), Kishanpur Wildlife Sanctuary (KIS), and Katarniaghat Wildlife Sanctuary (KAT) in the CTL of India during the dry winter months (December - March) of 2022 and 2024. The study area is a part of the larger Terai Arc Landscape (TAL) spanning from southern Nepal to the northern regions of India, is a biologically and ecologically rich floodplain landscape harbouring a wide range of flora and fauna ( Ram et al., 2021 ). Excessive habitat fragmentation and forest loss owing to human population expansion, agriculture and infrastructure has resulted in increased human-wildlife conflict and population reduction of various species ( Thapa et al., 2021 ), offering a compelling study landscape to infer the effects of fragmentation. This region forms the northernmost extent of the range of the sloth bear ( Melursus ursinus ), a large omnivore endemic to the Indian subcontinent with India harbouring 90% of its global population. Listed as vulnerable on the IUCN redlist, their populations are isolated across the country, primarily due to habitat fragmentation ( Dharaiya et al., 2017 ). Unlike other populations in India, sloth bears in this transboundary landscape with Nepal are restricted to protected areas with lack of reports of movement in human modified areas ( Sadadev et al., 2024 ) . The limited distribution of the species and predicted lack of movement across protected areas raise concern and underscore the need to investigate the genetic population structure and barriers to genetic connectivity in this fragmented landscape. Hence, suspected sloth bear faecal samples were collected non-invasively using sterile swabs and preserved in Longmire’s buffer ( Longmire et al., 1997 ) to understand population connectivity across the landscape. 2.2. Generating genetic data and identifying species DNA was extracted using Qiagen Blood and Tissue Extraction kit following the manufacturers protocol and concentrations were measured using Qubit Fluorometer 4.0 ( Invitrogen ). A two-step approach was adopted to genetically confirm the species origin of the suspected faecal samples. The extracted DNA was first screened with a species-specific COXII marker ( Thatte et al., 2018 ) and extracts that failed to amplify were subsequently tested with a more generic 16S rRNA marker ( Mukherjee et al., 2018 ). The 16S amplified products were sequenced in the in-house Sanger sequencing facility and sequences were matched to the NCBI genetic repository for robust species identification. Extracts that failed to amplify using the 16S rRNA primers were concluded to be poor-quality and discarded from downstream analyses. Owing to the presence of prey and bacterial contamination, faecal samples have low host DNA concentration. Thus, the samples genetically identified as belonging to sloth bears were enriched using methyl-CpG-binding domain (MBD) proteins to maximise host DNA concentration (Chiou & Bergey, 2016). For DNA enrichment, NEBNext Microbiome DNA enrichment kits were used ( Feehery et al., 2013 ), for which MBD2-Fc magnetic beads were prepared by the conjugation of MBD2Fc protein and protein A magnetic beads. Prior to enrichment, the extract volume was reduced from 200 to 40uL using the CentriVap DNA concentrator ( Labconco ) to concentrate the DNA. Post enrichment, the extracts were processed using double digested library preparation protocol ( Peterson et al., 2012 ), while incorporating some modifications specific to low hDNA concentration samples ( Tyagi et al., 2024 ). A combination of Sph1 and MluC1 restriction enzymes was used for digestion at 37°C for 3 hours followed by a 20 minutes heat-kill at 65°C, followed by adaptor ligation using T4 DNA Ligase at 23°C for 2 hours and 10 minutes at 65°C. Ligated products were purified to remove <100 bp fragments, followed by a 16-cycle indexing PCR (98°C for 30s, 65°C for 30s, 72°C for 30s and 72°C for 5min). Post indexing, fragments ranging from 200-500 bp were retained during purification (0.5X and 1X) with in-house prepared carboxyl group-coated SPRI beads. Individual sample libraries were quantified using a Qubit Fluorometer and their fragment size distribution was evaluated using the Agilent Tapestation. A detailed protocol is provided in the Supplementary Information (S3). DNA concentrations were measured after each processing step - i. extraction, ii. enrichment, and iii. library preparation - and products with unquantifiable DNA concentration were discarded. The remaining sample libraries were pooled in an equimolar fashion to generate comparable reads across varying sample concentrations. The pooled library was ultimately purified to retain fragments ranging from 200 to 500 bp, and was sequenced on the NovaSeq platform. Samples that yielded poor-quality data were resequenced to improve coverage. 2.3. Calling and filtering variants Demultiplexed reads were quality checked using FastQC and compiled to a single .html report using MultiQC ( Ewels et al., 2016 ). Adaptor sequences, low quality bases and very short reads were removed using Trimmomatic ( Bolger et al., 2014 ). As the reads were generated from faecal samples, contamination with prey as well as bacterial DNA was a major concern despite the enrichment. Hence, the raw reads were mapped to a reference genome of the closest related species ( Yu et al., 2004 ), sun bear Helarctos malayanus (GCA_028533245.1), in absence of a sloth bear reference genome, using BWA-MEM ( Li, 2013 ). The bam files, retaining only the mapped reads, were then converted to a .fastq file using BEDTools (Quinlan et al., 2010) to be used for downstream processing. The filtered reads were then processed using the ipyrad bioinformatic pipeline with 28 default parameters to conduct de-novo local assembly and analyze the ddRAD sequence data ( Eaton & Overcast, 2020 ) using default parameters to generate a Variant Call Format (VCF) file with SNPs. The filters ensured a minimum depth of six bases, 85% clustering threshold, and retention of loci present in a minimum of four samples. Additionally, reads with less than five low quality bases, phred score of less than 33, or length less than 35 were removed, and adaptors were trimmed. Further details of ipyrad parameters used can be found in the Supplementary section (S4). The output VCF file containing the variants was further filtered using vcftools ( Danecek et al., 2011 ). The final filtered vcf contained only biallelic sites with a minimum depth of six and maximum depth of 1000 copies and major allele count of more than three to avoid very rare or technical variants. Lastly, samples with over 60% and loci with over 40% missing data were dropped to remove poor quality data and reduce overall missingness. 2.4. Assessing genetic variation and differentiation Recaptures in the dataset could result in false inferences of genetic clusters, hence the genetic relatedness between samples was assessed to identify unique individuals and remove recaptures using KING: Relationship Inference Software ( Manichaikul et al., 2010 ). To identify genetic population clusters and understand admixture across sampled protected areas, ADMIXTURE and Discriminant analysis of Principal Components (DAPC) analyses were conducted. To investigate the extent of genetic mixing between populations and identify shared ancestries that influence genetic diversity, genetic admixture was analysed using ADMIXTURE ( Alexander et al., 2015 ) software. Using .bed files generated from the PLINK ( Purcell et al., 2007 ) software, this algorithm utilizes a maximum-likelihood framework to estimate the optimum number of clusters (k) by cross-validating the output across multiple runs (r = 25) using a range of k values (k=1-10). Further, a supervised classification step was incorporated using the Discriminant analysis of Principal Components (DAPC), based on 30 principal components, using predefined geographic labels (based on sampled protected areas), enabling a fine-scale detection of population structure, using the vcfR ( Knaus & Grünwald, 2016 ) and adgenet ( Jombart, 2008 ) package in R. The level of genetic variation within populations was assessed by calculating the mean observed (H O ) and expected (H E ) heterozygosity using the “ basic.stats ” function of the hierfstat ( Goudet, 2005 ) package in R, at each locus per population. To compare the degree of genetic variation within and between subpopulations, the fixation index (F ST ) was computed using the “ pairwise.WCfst() ” function, providing a measure of genetic differentiation and an indication of gene flow. Lastly, genetic and geographic distances were correlated to understand the effect of Isolation By Distance (IBD) in shaping the spatial population structure using mantel test from the ade4 package in R (Dray et al., 2007). 2.5. Modelling functional connectivity across landscape To understand the influence of landscape features on observed spatial population structure for the species, a landscape genetic approach was implemented. This involves parametrization of resistance surface for each landscape feature determining the resistance offered by the latter to movement of the species using pairwise genetic distances. Based on sloth bear ecology, landscape properties, and published literature, five uncorrelated landscape variables (distance to water, agriculture land cover density, road density, enhanced vegetation index, and distance to human settlement) relevant to understanding the connectivity of sloth bear populations were selected ( Table 1 ) ( Dutta et al., 2015 ; Thatte et al., 2020 ; Malik et al., 2023 ). As studies have shown that species-landscape interactions are scale-dependent ( Cushman & Landguth, 2010 ; Suárez-Castro et al., 2018 ), all variables were processed at four spatial scales - 1 km, 2 km, 5 km, and 10 km, using the terra package in R (Hijmas et al., 2022). All variables were reclassified and recalculated as explained in Table 1 . Distance layers were generated using the “Proximity (Raster Distance)” function in QGIS with a binary target layer as the input raster (further details can be found in Supplementary Section S6). View this table: View inline View popup Download powerpoint Table 1. Description and details of landscape variables used in the study. A multi-surface optimization approach was used to understand the effect of landscape features in governing gene flow, and to create a composite resistance surface using resistanceGA package in R ( Peterman, 2018 ). As sloth bears are primarily solitary in nature, individual-based analysis was implemented to understand landscape effect on gene flow across sampled individuals. Genetic distances were calculated between unique individuals using the “ diss.dist ” function of the poppr package in R ( Kamvar et al., 2014 ). The rasters for the landscape variables were converted to ascii format using the terra package in R (Hijmas et al., 2022). Using resistanceGA , a mixed effect linear mixed effects model was fitted to the data with a maximum likelihood population effects parameterization (MLPE), which has been identified as the optimal choice for landscape genetic analysis (Shirk et al., 2017). With genetic data as the response and effective cost distance within each landscape variable as predictors, single surface models were optimized with default parameters to attain lowest AIC values within a maximum of 1000 iterations. Hence, at every scale, each landscape variable was optimized to identify the transformation type, shape of the transformation, and the maximum resistance offered using the “ SS_optim” function in the R esistanceGA package in R ( Peterman, 2018 ). Therefore, using univariate optimization, scale and best-suited model parameters for each landscape variable were identified. The optimized single surface parameters were used to understand the influence of landscape variables on spatial genetic population structure. These optimized parameters for each scale-selected raster variable were further used to generate composite surfaces with all combinations of the five variables to optimize the composite landscape resistance layer using the “ MS_optim” function in the ResistanceGA package in R ( Peterman, 2018 ). The optimized multisurface raster layer with least AIC value was used to model functional connectivity across the landscape. Using the optimized multisurface resistance layer and protected area node raster, the connectivity was modelled for sloth bears using the Circuitscape software ( McRae et al., 2009 ). Both layers were converted to ascii format in the Universal Transverse Mercator projection to be used as input for pairwise current modelling in Circuitscape. 3. Results 3.1. Genetic data generation and species identification Out of the 209 suspected sloth bear samples collected, 189 samples were successfully identified using the two-step species-specific (n=126) and 16s (n=63) primer amplification steps. All confirmed sloth samples were subsequently enriched, out of which 111 samples with quantifiable post-enrichment concentrations were prepped and added to the final library pool owing to variable DNA quality and concentration. Post sequencing, poor-quality samples with little or no sequencing reads (<10MB) were dropped. A raw VCF file identifying a total of 6,97,579 SNPs across 94 individuals was generated using the Ipyrad pipeline, however it had very high missingness. Hence, post filtering, a final filtered vcf containing 964 SNPs across 66 samples was retained (see Table 2 ). View this table: View inline View popup Download powerpoint Table 2: Progression sloth bear samples through species identification, enrichment, library preparation and SNP recovery. 3.2. Genetic variation and differentiation All captured samples belonged to unique individuals with no recaptures, with a few first-, second-and third- degree relatives, and the majority of individuals being unrelated (Fig S5.1). The supervised clustering in the DAPC plot reveals a distinct separation between individuals of Dudhwa which formed a non-overlapping cluster, compared to those of Pilibit and Kishanpur in which partial overlap can be seen. In line with the DAPC findings, the admixture plot further illustrates genetic structuring based on individual ancestry composition. Optimal K based on least cross-validation (CV) error across ADMIXTURE runs was identified as four ( Fig 2A ). At K=3, Dudhwa individuals show predominantly a single ancestry. At the optimal K=4, individuals from Dudhwa exhibited dominant ancestry from a single cluster (dark green) with one probable F1 and one F3 individual. Kishanpur individuals also show a similar pattern with a dominant ancestry of a single cluster (pink) with several F1 and F2 individuals. Conversely, individuals from Pilibhit display mixed ancestry proportion from all four clusters ( Fig 2B ). The spatial genetic structure map, with pie charts representing admixture proportions of individuals across the landscape, further emphasises the local dominance of certain clusters ( Fig 3 ). Download figure Open in new tab Figure 1. Schematic explaining the methodological workflow. A) Non-invasive faecal sample collection of sloth bears using sterile swabs; B) Genetic data generation workflow (Preparation of ddRAD-sequencing libraries, mapping of sequenced reads to a closely related reference genome to filter sequences and de-novo clustering of filtered sequences); C) Identification and filtering of SNPs across individuals; D) Assessment of genetic variation and differentiation across sampled protected areas; E) Identification of gene flow barriers and modelling functional connectivity across the landscape Created in BioRender. Rana, D. (2025) https://BioRender.com/haignq6 Download figure Open in new tab Figure 2. Investigating population structure of sloth bear populations in CTL. A. Identifying optimal K based on cv error across admixture runs B. Admixture plot showing membership proportions for individuals under different optimal K across protected areas C. Discriminant analysis of principal components representing variation across groups based on supervised clustering across protected areas. Download figure Open in new tab Figure 3. Geographical distribution of genetic clusters of sloth bear populations based on the identified optimal K=4 across the sampled protected areas. Download figure Open in new tab Figure 4. Predicted connectivity map based on multisurface optimization of landscape variables. A. Circuit theory based connectivity map based on optimized composite resistance surface. B. Input landscape variables (n=5) with 5 km resolution used to build an optimized composite resistance surface. A mean observed heterozygosity (H O ) of 0.073 and a mean expected heterozygosity (H E ) of 0.156 was observed. High fixation index values were found across the protected areas with a mean inbreeding coefficient (F IS ) of 0.45. Pairwise F ST values show highest divergence between Dudhwa and Kishanpur (F ST = 0.30) and lowest divergence between Pilibhit and Kishanpur (F ST = 0.09) populations. A weak positive yet statistically insignificant correlation (r = 0.09; p = 0.042) was observed between genetic and geographic distances using the Mantel test. 3.3. Population connectivity patterns The univariate optimization revealed 5 km to be the best-suited scale for most variables. All landscape variables showed different relationships with genetic distance after single surface optimization. Agriculture showed a positive exponential relation, with resistance to movement increasing rapidly in areas with over 80% agriculture landcover density. With increasing distance to water, the resistance to gene flow decreased sharply with no resistance after 6km. On the other hand, resistance decreased with increasing values of EVI. The EVI in the landscape had a narrow range from 0.2 to 0.4, and the optimized parameters showed low resistance to movement in denser areas. Lastly, road density showed a negative exponential relation with high resistance recorded at densities of less than 10 km per grid whereas distance to settlement showed a positive exponential relation with high resistance values after 1 km (Fig S6.4). The optimized composite multisurface resistance layer had two landscape variables influencing genetic structure. Agriculture land cover density was the most important variable explaining the population structure with 95.79% variable contribution, followed by road density with 4.21% contribution. The modelled connectivity map shows overall weak connectivity across the landscape. The red areas, representing moderate connectivity, show maximum connectivity between PBT and KIS, whereas the predicted connectivity between DDW and KAT is limited. Transboundary connectivity with Nepal was also highlighted. 4. Discussion This study investigated the effects of landscape fragmentation on sloth bears in the CTL, a multiuse landscape at the northern periphery of the species’ distribution. By applying SNP-based approaches to non-invasively collected faecal samples, we optimized genetic protocols, assessed population structure and diversity, and identified landscape variables influencing connectivity. Our results revealed weak genetic structuring with evidence of admixture across protected areas, but overall extremely low genetic diversity and high inbreeding coefficients, suggestive of recent bottlenecks. Landscape genetic analyses identified agriculture density as the primary barrier to gene flow. Together, these findings suggest that fragmentation and range-edge effects are eroding genetic variation and isolating populations, raising conservation concerns for the long-term persistence of sloth bears in the CTL. 4.1. Non-invasive genomic approaches enable multiscale Insights This study marks the first application of SNP-based methods to investigate sloth bear populations and the first genomic assessment in the Indian Terai. Working with non-invasively collected faecal samples posed challenges due to degraded DNA, but protocol optimization significantly improved amplification success. Species identification using a sloth bear-specific primer yielded a 60.3% success rate (n = 126), lower than Thatte et al. (2018) , who reported 87%. However, our modified two-step approach achieved a 90.4% success rate (n = 189), representing a substantial improvement. Individual identification was complicated by the absence of a sloth bear genome assembly. We addressed this by combining reference-based filtering with de novo clustering, retaining ∼35% of samples for robust genomic analysis. This strategy enabled reliable individual identification and high-resolution assessments of genetic structure and diversity, demonstrating the feasibility of integrating non-invasive genomics into monitoring frameworks for elusive carnivores in fragmented landscapes. 4.2. Population genetic patterns suggest historic bottlenecks Our analyses revealed admixture among four genetic clusters with weak spatial structuring across protected areas. Among the sampled sites, DDW showed the most homogeneous ancestry, KIS reflected a distinct cluster, and PBT exhibited high levels of admixture. These patterns are consistent with geography - gene flow appears more likely between the contiguous PBT-KIS populations than between either of them and DDW, which is more isolated. Pairwise FST estimates supported this interpretation, with lower differentiation between PBT-KIS relative to DDW. Despite weak structuring, both observed (0.08) and expected (0.17) heterozygosity was markedly low, and the inbreeding coefficient was unusually high (FIS = 0.47). These results suggest reduced genetic variability, recent bottlenecks, and possibly ongoing inbreeding. Historical context provides additional insight where the CTL underwent drastic land-use changes in the 1970s, following large-scale post-partition human migration and agricultural expansion (Bahadur K.C., 2011). This fragmentation likely disrupted connectivity within what may once have been a panmictic population, producing the current genetic signatures. The limited population genetic studies on sloth bears across the country recorded moderate to high genetic diversity (central India: H E =0.72, H O =0.53; western Indian: H E =0.18-0.8, H O =0.46-0.84) and low differentiation using microsatellite markers ( Dutta et al., 2015 ; Gomes et al., 2024 ; Sharma et al., 2013; Thatte et al., 2020 ). However, Paudel (2023) , focusing on the neighbouring sloth bear populations in the Nepal Terai, observed lower low genetic diversity (H O =0.33) with a positive inbreeding coefficient (F IS = 0.08). These results reinforce our findings indicating that peripheral Terai populations are genetically eroded compared to those in Central and Western India. 4.3. Anthropogenic barriers drive genetic isolation Given the weak structuring, high inbreeding, and absence of isolation-by-distance, we examined landscape variables influencing genetic differentiation. Landscape genetics offers a powerful framework to assess how environmental features shape gene flow ( Manel et al., 2003 ), guiding corridor design and conservation prioritization (Sharma et al., 2013). Despite their potential, very few landscape genetic studies have been conducted in the fragmented landscapes of India ( Thatte et al., 2020 ; Tyagi et al., 2024 ) and are virtually absent in the CTL, thereby highlighting a critical gap in understanding how fragmentation may influence gene flow in this region. In the broader Terai Arc Landscape, the focus of landscape connectivity studies has been limited to tigers ( Anwar & Borah, 2019 ; Biswas et al., 2022 ) and elephants ( Mukherjee et al., 2025 ; Neupane et al., 2022 ). Our study is the first to use landscape genetic approaches to understand gene flow and its drivers in the Terai. The multisurface analysis revealed agriculture density as the dominant barrier to gene flow in the landscape, explaining over 95% of the variation, followed by road density. Other variables, like distance to water and EVI, also showed strong trends, with areas near water and sparse vegetation offering high resistance. These findings align with global bear studies that identify sparse vegetation, anthropogenic land use and water bodies as significant movement barriers ( Douchinsky, 2024 ; Lewis, 2012 ; Thatte et al., 2020 ). Counterintuitive findings for road density and distance to settlement, may reflect their skewed distribution as the sampling distribution was limited to protected areas. The landscape’s fragmentation, driven by agricultural expansion over recent decades, likely underpins the observed isolation and suggested bottlenecks. However, our parsimonious landscape genetic models explained less than 10% of genetic distance variation, suggesting that drift and historical bottlenecks are the primary drivers of spatial genetic patterns. This extends indirect support to the central marginal hypothesis, which suggests low diversity and high differentiation in peripheral populations owing to the historical bottlenecks and founder effects. 4.4. Peripheral populations are genetically compromised The Terai harbours the northern-most extreme of sloth bear populations in India. Unlike the contiguous metapopulations like in Central India, this region has undergone rapid fragmentation due to agriculture, infrastructure, and settlement expansion. Our findings, one of the lowest recorded measures of diversity for bears (H O =0.073) and a very high inbreeding coefficient across the landscape (F IS =0.45), highlight severe genetic erosion. While strong population structure was not detected, likely due to the recent fragmentation, the low diversity and elevated inbreeding suggest emerging isolation. Field surveys corroborate this vulnerability - Katarniaghat WLS showed no sloth bear presence despite being ∼20 km from DDW, and Paudel (2023) reported only two individuals in adjacent Bardia NP, Nepal. Connectivity modelling predicts extremely low movement potential between DDW and KAT, limiting prospects for natural demographic or genetic rescue. If fragmentation persists, these isolated units may face heightened extinction risk. These findings underscore the urgency of integrating genetic monitoring with habitat management strategies to assess their vulnerability, especially in range-edge populations. This enables the implementation of informed management strategies to ensure the long-term survival of these species in large landscapes. With extensive landscape modification recorded not more than 50 years back, we suggest that the rapid loss of genetic diversity and inbreeding, though triggered by landscape fragmentation, is primarily an artefact of historic bottlenecks leading to reduced effective population sizes in range peripheries as shown by many studies ( Eckert et al., 2008 ; Quemere et al., 2010 ; Arriagada et al., 2025 ). Our findings support the hypothesis and underscore the importance of functional connectivity specifically in peripheral populations. These findings underscore the importance of genetic monitoring with targeted prioritization of populations surviving in fragmented landscapes in range peripheries to mitigate extinction risk. Author Contributions DR led project conceptualization, data collection, analyses, and manuscript writing. JCS led the data generation and co-led manuscript writing. UR assisted in conceptualization while providing critical inputs and logistical support for the project. All authors critically contributed to the drafts and approved the final version for publication. All authors were engaged in the study from the beginning to provide a broad and holistic perspective to the designing and execution of the study. The authors declare no conflicting interests. Acknowledgement DR is supported by the TIFR-NCBS graduate program. We acknowledge the International Bear Association research and conservation grant 2025-26 awarded to DR for their funding support towards the project. Our gratitude extends to the National Centre for Biological Sciences for their institutional support as facilitated by UR, specifically the Next-Generation Genomics Facility (NGGF, Bangalore Life Science Cluster, BLiSC) for their assistance in data generation. The NCBS data cluster, supported under project no. 12-R&D-TFR-5.04-0900 by the Department of Atomic Energy, Government of India, was indispensable. We would like to thank the Uttar Pradesh Forest Department for providing necessary permits to collect non-invasive samples (permit no. 1148/ 23-2-12) and their on-site assistance during the field season. DR would like to thank the field team - student interns and local field assistants, who tirelessly contributed to the intensive field surveys. We would like to thank Dr. Laura Bertola and Dr. Isaac Overcast for their suggestions regarding the bioinformatic analysis. Funder Information Declared International Association for Bear Research and Management, https://ror.org/056pa7468 Footnotes https://github.com/divyashreerana/publications/tree/8e915bc43fd80da99f7b7f3de01ecd2bae048cea/Rana-et-al_2025_Bear_landscape_genetics References ↵ Abeli , T. , Gentili , R. , Mondoni , A. , Orsenigo , S. , & Rossi , G. ( 2014 ). Effects of marginality on plant population performance . Journal of Biogeography , 41 ( 2 ), 239 – 249 . OpenUrl CrossRef ↵ Alexander , D. H. , Shringarpure , S. S. , Novembre , J. , & Lange , K. ( 2015 ). Admixture 1.3 software manual . Los Angeles : UCLA Human Genetics Software Distribution . ↵ Allendorf , F. W. ( 1986 ). Genetic drift and the loss of alleles versus heterozygosity . Zoo Biology , 5 ( 2 ), 181 – 190 . doi: 10.1002/zoo.1430050212 OpenUrl CrossRef Web of Science ↵ Allendorf , F. W. , Hössjer , O. , & Ryman , N. ( 2024 ). What does effective population size tell us about loss of allelic variation? Evolutionary Applications , 17 ( 6 ), e13733 . doi: 10.1111/eva.13733 OpenUrl CrossRef PubMed ↵ Anwar , M. , & Borah , J. ( 2019 ). Functional status of a wildlife corridor with reference to Tiger in Terai Arc Landscape of India . Tropical Ecology , 60 ( 4 ), 525 – 531 . OpenUrl ↵ Araya-Ajoy , Y. G. , Hansson Frank , T. , Burnett , H. , Søraker , J. S. , Ranke , P. S. , Goedert , D. , Ringsby , T.-H. , Jensen , H. , & Sæther , B.-E. ( 2025 ). Assessing the “Small Population” Paradigm: The Effects of Stochasticity on Evolutionary Change and Population Growth in a Bird Metapopulation . Ecology Letters , 28 ( 4 ), e70090 . doi: 10.1111/ele.70090 OpenUrl CrossRef PubMed ↵ Arriagada , A. , Canales-Aguirre , C. B. , Fuentes , N. , Saucedo , C. , & Colihueque , N. ( 2025 ). Phylogeography and Past Distribution of Peripheral Individuals of Large Hairy Armadillo Chaetophractus villosus . Diversity , 17 ( 6 ), 390 . OpenUrl Bahadur K.C. K. ( 2011 ). Linking physical, economic and institutional constraints of land use change and forest conservation in the hills of Nepal . Forest Policy and Economics , 13 ( 8 ), 603 – 613 . doi: 10.1016/j.forpol.2011.07.010 OpenUrl CrossRef ↵ Biswas , S. , Bhatt , S. , Sarkar , D. , Talukdar , G. , Pandav , B. , & Mondol , S. ( 2022 ). Assessing tiger corridor functionality with landscape genetics and modelling across Terai-Arc Landscape, India . Conservation Genetics , 23 ( 5 ), 949 – 966 . OpenUrl ↵ Bolger , A. M. , Lohse , M. , & Usadel , B. ( 2014 ). Trimmomatic: A flexible trimmer for Illumina sequence data . Bioinformatics , 30 ( 15 ), 2114 – 2120 . doi: 10.1093/bioinformatics/btu170 OpenUrl CrossRef PubMed Web of Science ↵ Bourgeois , S. , Kaden , J. , Senn , H. , Bunnefeld , N. , Jeffery , K. J. , Akomo-Okoue , E. F. , Ogden , R. , & McEwing , R. ( 2019 ). Improving cost-efficiency of faecal genotyping: New tools for elephant species . PLoS ONE , 14 ( 1 ), e0210811 . doi: 10.1371/journal.pone.0210811 OpenUrl CrossRef PubMed ↵ Brown , J. H. ( 1984 ). On the relationship between abundance and distribution of species . The American Naturalist , 124 ( 2 ), 255 – 279 . OpenUrl CrossRef Web of Science ↵ Brzosko , E. , Wróblewska , A. , Ratkiewicz , M. , Till-Bottraud , I. , Nicole , F. , & Baranowska , U. ( 2009 ). Genetic diversity of Cypripedium calceolus at the edge and in the centre of its range in Europe . 46 ( 3 ), 201 – 214 . OpenUrl ↵ Cardillo , M. , Purvis , A. , Sechrest , W. , Gittleman , J. L. , Bielby , J. , & Mace , G. M. ( 2004 ). Human population density and extinction risk in the world’s carnivores . PLoS Biology , 2 ( 7 ), e197 . OpenUrl CrossRef PubMed Chiou , K. L. , & Bergey , C. M. ( 2018 ). Methylation-based enrichment facilitates low-cost, noninvasive genomic scale sequencing of populations from feces . Scientific Reports , 8 ( 1 ), 1975 . doi: 10.1038/s41598-018-20427-9 OpenUrl CrossRef ↵ Cushman , S. A. , & Landguth , E. L. ( 2010 ). Spurious correlations and inference in landscape genetics . Molecular Ecology , 19 ( 17 ), 3592 – 3602 . OpenUrl CrossRef PubMed Web of Science ↵ Danecek , P. , Auton , A. , Abecasis , G. , Albers , C. A. , Banks , E. , DePristo , M. A. , Handsaker , R. E. , Lunter , G. , Marth , G. T. , Sherry , S. T. , McVean , G. , Durbin , R. , & 1000 Genomes Project Analysis Group . ( 2011 ). The variant call format and VCFtools . Bioinformatics , 27 ( 15 ), 2156 – 2158 . doi: 10.1093/bioinformatics/btr330 OpenUrl CrossRef PubMed Web of Science ↵ De Barba , M. , Miquel , C. , Lobréaux , S. , Quenette , P. Y. , Swenson , J. E. , & Taberlet , P. ( 2017 ). High-throughput microsatellite genotyping in ecology: Improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA . Molecular Ecology Resources , 17 ( 3 ), 492 – 507 . doi: 10.1111/1755-0998.12594 OpenUrl CrossRef PubMed ↵ Dharaiya , N. , Bargali , H. , & Sharp , T. ( 2017 ). Sloth Bear Global Assessment—THE IUCN RED LIST OF THREATENED SPECIES™ . doi: 10.13140/RG.2.2.20379.36646 OpenUrl CrossRef ↵ Douchinsky , P. R. ( 2024 ). Comparative Landscape Genetics and Dynamics in Demography Within Sympatric Ursids in Southeast Alaska . The University of Memphis . Dray , S. , Pélissier , R. , Couteron , P. , Fortin , M.-J. , Legendre , P. , Peres-Neto , P. R. , Bellier , E. , Bivand , R. , Blanchet , F. G. , & De Cáceres , M. ( 2012 ). Community ecology in the age of multivariate multiscale spatial analysis . Ecological Monographs , 82 ( 3 ), 257 – 275 . OpenUrl CrossRef Web of Science ↵ Dutta , T. , Sharma , S. , Maldonado , J. E. , Panwar , H. S. , & Seidensticker , J. ( 2015 ). Genetic Variation, Structure, and Gene Flow in a Sloth Bear (Melursus ursinus) Meta-Population in the Satpura-Maikal Landscape of Central India . PLOS ONE , 10 ( 5 ), e0123384 . doi: 10.1371/journal.pone.0123384 OpenUrl CrossRef PubMed ↵ Eaton , D. A. R. , & Overcast , I. ( 2020 ). ipyrad: Interactive assembly and analysis of RADseq datasets . Bioinformatics , 36 ( 8 ), 2592 – 2594 . doi: 10.1093/bioinformatics/btz966 OpenUrl CrossRef ↵ Eckert , C. G. , Samis , K. E. , & Lougheed , S. C. ( 2008 ). Genetic variation across species’ geographical ranges: The central–marginal hypothesis and beyond . Molecular Ecology , 17 ( 5 ), 1170 – 1188 . OpenUrl CrossRef PubMed Web of Science ↵ Estoup , A. , Jarne , P. , & Cornuet , J.-M. ( 2002 ). Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis . Molecular Ecology , 11 ( 9 ), 1591 – 1604 . doi: 10.1046/j.1365-294X.2002.01576.x OpenUrl CrossRef PubMed Web of Science ↵ Ewels , P. , Magnusson , M. , Lundin , S. , & Käller , M. ( 2016 ). MultiQC: Summarize analysis results for multiple tools and samples in a single report . Bioinformatics , 32 ( 19 ), 3047 – 3048 . doi: 10.1093/bioinformatics/btw354 OpenUrl CrossRef PubMed ↵ Fahrig , L. ( 2003 ). Effects of Habitat Fragmentation on Biodiversity . Annual Review of Ecology, Evolution, and Systematics , 34 (Volume 34, 2003), 487 – 515 . doi: 10.1146/annurev.ecolsys.34.011802.132419 OpenUrl CrossRef Web of Science ↵ Feehery , G. R. , Yigit , E. , Oyola , S. O. , Langhorst , B. W. , Schmidt , V. T. , Stewart , F. J. , Dimalanta , E. T. , Amaral-Zettler , L. A. , Davis , T. , Quail , M. A. , & Pradhan , S. ( 2013 ). A Method for Selectively Enriching Microbial DNA from Contaminating Vertebrate Host DNA . PLOS ONE , 8 ( 10 ), e76096 . doi: 10.1371/journal.pone.0076096 OpenUrl CrossRef PubMed ↵ Francis , R. A. , Millington , J. D. , & Chadwick , M. A. ( 2016 ). Urban landscape ecology . Routledge : Abingdon, UK New York, NY, USA . ↵ Frankham , R. ( 1998 ). Inbreeding and Extinction: Island Populations . Conservation Biology , 12 ( 3 ), 665 – 675 . doi: 10.1111/j.1523-1739.1998.96456.x OpenUrl CrossRef Web of Science ↵ Gassert , F. , Schulte , U. , Husemann , M. , Ulrich , W. , Rödder , D. , Hochkirch , A. , Engel , E. , Meyer , J. , & Habel , J. C. ( 2013 ). From southern refugia to the northern range margin: Genetic population structure of the common wall lizard, Podarcis muralis . Journal of Biogeography , 40 ( 8 ), 1475 – 1489 . OpenUrl ↵ Gomes , L. , Modi , S. , Nigam , P. , & Habib , B. ( 2024 ). The Bear Truth: Analyzing Genetic Variability and Population Structure in Sloth Bear across the Vidarbha Landscape Using Microsatellite Markers . Diversity , 16 ( 2 ), Article 2. doi: 10.3390/d16020074 OpenUrl CrossRef ↵ Goudet , J. ( 2005 ). Hierfstat, a package for R to compute and test hierarchical F-statistics . Molecular Ecology Notes , 5 ( 1 ), 184 – 186 . OpenUrl CrossRef PubMed Web of Science ↵ Hasui , É. , Martensen , A. C. , Uezu , A. , Pimentel , R. G. , Ramos , F. N. , Ribeiro , M. C. , & Metzger , J. P. ( 2024 ). Populations across bird species distribution ranges respond differently to habitat loss and fragmentation: Implications for conservation strategies . Perspectives in Ecology and Conservation , 22 ( 1 ), 43 – 54 . OpenUrl ↵ Hedrick , P. W. , Peterson , R. O. , Vucetich , L. M. , Adams , J. R. , & Vucetich , J. A. ( 2014 ). Genetic rescue in Isle Royale wolves: Genetic analysis and the collapse of the population . Conservation Genetics , 15 ( 5 ), 1111 – 1121 . OpenUrl Hijmans , R. J. , Bivand , R. , Forner , K. , Ooms , J. , Pebesma , E. , & Sumner , M. D. ( 2022 ). Package ‘terra.’ Maintainer : Vienna, Austria , 384 . ↵ Johnson , W. E. , Onorato , D. P. , Roelke , M. E. , Land , E. D. , Cunningham , M. , Belden , R. C. , McBride , R. , Jansen , D. , Lotz , M. , Shindle , D. , Howard , J. , Wildt , D. E. , Penfold , L. M. , Hostetler , J. A. , Oli , M. K. , & O’Brien , S. J. ( 2010 ). Genetic Restoration of the Florida Panther . Science , 329 ( 5999 ), 1641 – 1645 . doi: 10.1126/science.1192891 OpenUrl Abstract / FREE Full Text ↵ Jombart , T. ( 2008 ). adegenet: A R package for the multivariate analysis of genetic markers . Bioinformatics , 24 ( 11 ), 1403 – 1405 . doi: 10.1093/bioinformatics/btn129 OpenUrl CrossRef PubMed Web of Science ↵ Jones , K. S. , Aunins , A. W. , Young , C. C. , Johnson , R. L. , & Morrison , C. L. ( 2025 ). Population genetics of the endangered narrowly endemic Island Marble butterfly (Euchloe ausonides insulanus) . bioRxiv , 2025 – 05 . ↵ Kamvar , Z. N. , Tabima , J. F. , & Grünwald , N. J. ( 2014 ). Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction . PeerJ , 2 , e281 . OpenUrl CrossRef PubMed ↵ Knaus , B. , & Grünwald , N. ( 2016 ). VcfR: An R package to manipulate and visualize VCF format data . doi: 10.1101/041277 OpenUrl Abstract / FREE Full Text ↵ Lewis , T. M. ( 2012 ). Shoreline distribution and landscape genetics of bears in a recently deglaciated fjord: Glacier Bay, Alaska . ↵ Li , H. ( 2013 ). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM (No . arXiv: 1303.3997 ). arXiv. doi: 10.48550/arXiv.1303.3997 OpenUrl CrossRef ↵ Longmire , J. L. , Maltbie , M. , & Baker , R. J. ( 1997 ). Use of” lysis buffer” in DNA isolation and its implication for museum collections . ↵ Malik , A. , Singh , C. , Dharaiya , N. , & Padate , G. ( 2023 ). Potential connectivity for sloth bear habitats in Western India’s landscape . European Journal of Wildlife Research , 69 ( 6 ), 118 . OpenUrl ↵ Manel , S. , Schwartz , M. K. , Luikart , G. , & Taberlet , P. ( 2003 ). Landscape genetics: Combining landscape ecology and population genetics . Trends in Ecology & Evolution , 18 ( 4 ), 189 – 197 . doi: 10.1016/S0169-5347(03)00008-9 OpenUrl CrossRef Web of Science ↵ Manichaikul , A. , Mychaleckyj , J. C. , Rich , S. S. , Daly , K. , Sale , M. , & Chen , W.-M. ( 2010 ). Robust relationship inference in genome-wide association studies . Bioinformatics , 26 ( 22 ), 2867 – 2873 . OpenUrl CrossRef PubMed Web of Science ↵ McRae , B. H. , Shah , V. B. , & Mohapatra , T. ( 2009 ). Circuitscape user’s guide . The University of California, Santa Barbara . ↵ Mukherjee , C. , Beall , C. J. , Griffen , A. L. , & Leys , E. J. ( 2018 ). High-resolution ISR amplicon sequencing reveals personalized oral microbiome . Microbiome , 6 ( 1 ), 153 . OpenUrl CrossRef PubMed ↵ Mukherjee , T. , Abedin , I. , Ghosh , A. , Kim , H.-W. , & Kundu , S. ( 2025 ). Elucidating the complexity of transboundary functional corridor networks for Asian Elephants Elephas maximus through ecological and genetic insights in Asia . ↵ Naidoo , R. , Kilian , J. W. , Du Preez , P. , Beytell , P. , Aschenborn , O. , Taylor , R. D. , & Stuart-Hill , G. ( 2018 ). Evaluating the effectiveness of local- and regional-scale wildlife corridors using quantitative metrics of functional connectivity . Biological Conservation , 217 , 96 – 103 . doi: 10.1016/j.biocon.2017.10.037 OpenUrl CrossRef ↵ Natesh , M. , Taylor , R. W. , Truelove , N. K. , Hadly , E. A. , Palumbi , S. R. , Petrov , D. A. , & Ramakrishnan , U. ( 2019 ). Empowering conservation practice with efficient and economical genotyping from poor quality samples . Methods in Ecology and Evolution , 10 ( 6 ), 853 – 859 . OpenUrl ↵ Neupane , D. , Baral , S. , Risch , T. S. , & Campos-Arceiz , A. ( 2022 ). Broad scale functional connectivity for Asian elephants in the Nepal-India transboundary region . Journal of Environmental Management , 321 , 115921 . OpenUrl PubMed ↵ O’Brien , C. D. , Hall , J. E. , Orchard , D. , Barratt , C. D. , Arntzen , J. W. , & Jehle , R. ( 2015 ). Extending the natural range of a declining species: Genetic evidence for native great crested newt (Triturus cristatus) populations in the Scottish Highlands . European Journal of Wildlife Research , 61 ( 1 ), 27 – 33 . doi: 10.1007/s10344-014-0863-7 OpenUrl CrossRef ↵ Paudel , R. P. ( 2023 ). Sloth bears (Melursus ursinus) in Nepal: Ecology, genetic diversity, and human-sloth bear conflict . ↵ Peterman , W. E. ( 2018 ). ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms . Methods in Ecology and Evolution , 9 ( 6 ), 1638 – 1647 . doi: 10.1111/2041-210X.12984 OpenUrl CrossRef ↵ Peterson , B. K. , Weber , J. N. , Kay , E. H. , Fisher , H. S. , & Hoekstra , H. E. ( 2012 ). Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species . PLOS ONE , 7 ( 5 ), e37135 . doi: 10.1371/journal.pone.0037135 OpenUrl CrossRef PubMed ↵ Purcell , S. , Neale , B. , Todd-Brown , K. , Thomas , L. , Ferreira , M. A. , Bender , D. , Maller , J. , Sklar , P. , De Bakker , P. I. , & Daly , M. J. ( 2007 ). PLINK: a tool set for whole-genome association and population-based linkage analyses . The American Journal of Human Genetics , 81 ( 3 ), 559 – 575 . OpenUrl CrossRef PubMed ↵ Quemere , E. , Crouau-Roy , B. , Rabarivola , C. , Louis Jr , E. E. , & Chikhi , L. ( 2010 ). Landscape genetics of an endangered lemur (Propithecus tattersalli) within its entire fragmented range . Molecular Ecology , 19 ( 8 ), 1606 – 1621 . OpenUrl CrossRef PubMed Web of Science Quinlan , A. R. , & Hall , I. M. ( 2010 ). BEDTools: A flexible suite of utilities for comparing genomic features . Bioinformatics , 26 ( 6 ), 841 – 842 . doi: 10.1093/bioinformatics/btq033 OpenUrl CrossRef PubMed Web of Science ↵ Ram , A. K. , Yadav , N. K. , Kandel , P. N. , Mondol , S. , Pandav , B. , Natarajan , L. , Subedi , N. , Naha , D. , Reddy , C. S. , & Lamichhane , B. R. ( 2021 ). Tracking forest loss and fragmentation between 1930 and 2020 in Asian elephant (Elephas maximus) range in Nepal . Scientific Reports , 11 ( 1 ), 19514 . doi: 10.1038/s41598-021-98327-8 OpenUrl CrossRef PubMed ↵ Rana , D. , Boyer , F. , Barba , M. D. , Taberlet , P. , & Ramakrishnan , U. ( 2024 ). From single species to communities: Microsatellite amplicon sequencing to monitor felids using Feliplex . bioRxiv , 2024 – 10 . ↵ Sadadev , B. M. , Silwal , T. , Dhami , B. , Neupane , D. , & Bryan , H. ( 2024 ). First sighting of a sloth bear in a decade in Shuklaphanta National Park, Nepal . Ursus , 2024 ( 35e3 ), 1 – 5 . OpenUrl ↵ Sagar , V. , Kaelin , C. B. , Natesh , M. , Reddy , P. A. , Mohapatra , R. K. , Chhattani , H. , Thatte , P. , Vaidyanathan , S. , Biswas , S. , & Bhatt , S. ( 2021 ). High frequency of an otherwise rare phenotype in a small and isolated tiger population . Proceedings of the National Academy of Sciences , 118 ( 39 ), e2025273118 . OpenUrl Abstract / FREE Full Text ↵ Sexton , J. P. , McIntyre , P. J. , Angert , A. L. , & Rice , K. J. ( 2009 ). Evolution and ecology of species range limits . Annu. Rev. Ecol. Evol. Syst ., 40 , 415 – 436 . OpenUrl CrossRef Web of Science Sharma , S. , Dutta , T. , Maldonado , J. E. , Wood , T. C. , Panwar , H. S. , & Seidensticker , J. ( 2013a ). Forest corridors maintain historical gene flow in a tiger metapopulation in the highlands of central India . Proceedings of the Royal Society B: Biological Sciences , 280 ( 1767 ), 20131506 . OpenUrl CrossRef PubMed Sharma , S. , Dutta , T. , Maldonado , J. E. , Wood , T. C. , Panwar , H. S. , & Seidensticker , J. ( 2013b ). Selection of microsatellite loci for genetic monitoring of sloth bears . Ursus , 164 – 169 . ↵ Shirk , A. J. , Landguth , E. L. , & Cushman , S. A. ( 2018 ). A comparison of regression methods for model selection in individual-based landscape genetic analysis . Molecular Ecology Resources , 18 ( 1 ), 55 – 67 . doi: 10.1111/1755-0998.12709 OpenUrl CrossRef PubMed ↵ Sittenthaler , M. , Schöll , E. M. , Leeb , C. , Haring , E. , Parz-Gollner , R. , & Hackländer , K. ( 2020 ). Factors influencing genotyping success and genotyping error rate of Eurasian otter (Lutra lutra) faeces collected in temperate Central Europe . European Journal of Wildlife Research , 67 ( 1 ), 2 . doi: 10.1007/s10344-020-01444-4 OpenUrl CrossRef ↵ Suárez-Castro , A. F. , Simmonds , J. S. , Mitchell , M. G. , Maron , M. , & Rhodes , J. R. ( 2018 ). The scale-dependent role of biological traits in landscape ecology: A review . Current Landscape Ecology Reports , 3 ( 1 ), 12 – 22 . OpenUrl ↵ Taberlet , P. , Waits , L. P. , & Luikart , G. ( 1999 ). Noninvasive genetic sampling: Look before you leap . Trends in Ecology & Evolution , 14 ( 8 ), 323 – 327 . OpenUrl PubMed ↵ Thapa , K. , Bhandari , A. , Thapa , M. , Kuikel , J. , Sada , R. , Rai , D. , Malla , S. , Gotame , B. , Tuladhar , S. , Nepal , S. , Sigdel , E. , & Neupane , P. ( 2021 ). Connecting Corridors . ↵ Thatte , P. , Chandramouli , A. , Tyagi , A. , Patel , K. , Baro , P. , Chhattani , H. , & Ramakrishnan , U. ( 2020 ). Human footprint differentially impacts genetic connectivity of four wide-ranging mammals in a fragmented landscape . Diversity and Distributions , 26 ( 3 ), 299 – 314 . doi: 10.1111/ddi.13022 OpenUrl CrossRef ↵ Thatte , P. , Patel , K. , & Ramakrishnan , U. ( 2018 ). Rapid species identification of sloth bears from non-invasive samples: A PCR-based assay . Ursus , 29 ( 1 ), 67 – 70 . doi: 10.2192/URSUS-D-17-00024.2 OpenUrl CrossRef ↵ Tyagi , A. , Khan , A. , Thatte , P. , & Ramakrishnan , U. ( 2022 ). Genome-wide single nucleotide polymorphism (SNP) markers from fecal samples reveal anthropogenic impacts on connectivity: Case of a small carnivore in the central Indian landscape . Animal Conservation , 25 ( 5 ), 648 – 659 . doi: 10.1111/acv.12770 OpenUrl CrossRef ↵ Tyagi , A. , Yadav , N. , Pandit , A. , & Ramakrishnan , U. ( 2024 ). On the road to losing connectivity: Faecal samples provide genome-wide insights into anthropogenic impacts on two large herbivore species in central India . Molecular Ecology , 33 ( 16 ), e17461 . doi: 10.1111/mec.17461 OpenUrl CrossRef ↵ van Strien , M. J. , Keller , D. , Holderegger , R. , Ghazoul , J. , Kienast , F. , & Bolliger , J. ( 2014 ). Landscape genetics as a tool for conservation planning: Predicting the effects of landscape change on gene flow . Ecological Applications , 24 ( 2 ), 327 – 339 . doi: 10.1890/13-0442.1 OpenUrl CrossRef PubMed ↵ Wall , N. M. , Davy , C. M. , Fortin , M.-J. , & James , P. M. A. ( 2025 ). Genetic effects of landscape change on three sympatric turtle species in southern Ontario, Canada . Conservation Genetics . doi: 10.1007/s10592-025-01715-0 OpenUrl CrossRef ↵ Wasserman , T. , Cushman , S. , Shirk , A. , Landguth , E. , & Littell , J. ( 2012 ). Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA . Landscape Ecology , 27 ( 2 ), 211 – 225 . OpenUrl CrossRef ↵ Yu , L. , Li , Q. , Ryder , O. A. , & Zhang , Y. ( 2004 ). Phylogeny of the bears (Ursidae) based on nuclear and mitochondrial genes . Molecular Phylogenetics and Evolution , 32 ( 2 ), 480 – 494 . doi: 10.1016/j.ympev.2004.02.015 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted September 29, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Genetic Erosion at the Edge: Landscape Fragmentation and Connectivity in Sloth Bears of the Indian Terai Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genetic Erosion at the Edge: Landscape Fragmentation and Connectivity in Sloth Bears of the Indian Terai Divyashree Rana , Judi Clarissa Samjith , Uma Ramakrishnan bioRxiv 2025.09.27.678944; doi: https://doi.org/10.1101/2025.09.27.678944 Share This Article: Copy Citation Tools Genetic Erosion at the Edge: Landscape Fragmentation and Connectivity in Sloth Bears of the Indian Terai Divyashree Rana , Judi Clarissa Samjith , Uma Ramakrishnan bioRxiv 2025.09.27.678944; doi: https://doi.org/10.1101/2025.09.27.678944 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Ecology Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41911) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13371) Ecology (19887) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22482) Immunology (17728) Microbiology (40363) Molecular Biology (17163) Neuroscience (88536) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15129) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9817) Zoology (2269)