Population genetics and lineage structure of the endangered Bolivian chinchilla rat Abrocoma boliviensis

Population genetics and lineage structure of the endangered Bolivian chinchilla rat Abrocoma boliviensis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population genetics and lineage structure of the endangered Bolivian chinchilla rat Abrocoma boliviensis Daniela Arenas-Viveros, Teresa Tarifa, Marisol Hidalgo-Cossio, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6513653/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2026 Read the published version in Conservation Genetics → Version 1 posted 10 You are reading this latest preprint version Abstract Studies on conservation genetics of endangered species have the ability to identify which populations should be the focus of management plans. The Bolivian chinchilla rat, Abrocoma boliviensis , is currently threatened by its rarity, paucity of information about its natural history, and landscape transformation driven by anthropogenic activities. Given the conservation status and limited distribution of A. boliviensis , understanding how its genetic diversity is apportioned is crucial to inform any potential conservation efforts. In this study, we assessed the genetic diversity and population structure of A. boliviensis as a first approximation to a comprehensive evaluation of the species. Mitochondrial data from 11 individuals of A. boliviensis reveal high levels of genetic distance, nucleotide diversity and polymorphisms, all of which indicate the existence of three separate clades. This is further supported by reduced representation genomic data that shows little to no admixture between these clades, suggesting that these lineages have been on separate evolutionary pathways and should be identified, at minimum, as separate evolutionary significant units. Our contribution highlights the urgency with which survey efforts must become the first order of action, and how new population-level data will provide a better understanding of the species, the evolutionary trajectory of its lineages, and the steps to take towards its conservation. Abrocomidae South America Andes EDGE species population structure Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The Bolivian chinchilla rat ( Abrocoma boliviensis ) is a rare and endemic species, found only in mid-to-high elevation forests of the eastern Andes in Bolivia (Hidalgo-Cossio et al. 2016 ; Quinteros-Muñoz 2015 ; Quiroga Pacheco et al. 2020 ; Tarifa et al. 2009 ). Rarity in a species can be determined based on abundance or restricted geography, and while not all rare species are necessarily at the risk of extinction, their low abundance and/or constrained distributions do make them vulnerable to stochastic events (Drever et al. 2012 ). In the latest assessment of A. boliviensis for the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, the distribution of A. boliviensis is limited to the type locality in the department of Santa Cruz and is classified as critically endangered under criteria B1ab(i,ii,ii) (Bernal 2016 ). This means that the species’ range extent is smaller than 100 km2, severely fragmented or known to exist at a single location, and its habitat presents a continuing decline in area, extent and/or quality. Further, the species has been recognized as one of the 100 EDGE mammal species in the world, a list that comprises species recognized as evolutionarily highly distinct and threatened with extinction (Gumbs et al. 2023 ). In recent years, sampling efforts have added at least eight new localities in the departments of Potosí, Cochabamba, Tarija, and Santa Cruz (Hidalgo-Cossio et al. 2016 ; Quinteros-Muñoz 2015 ; Quiroga Pacheco et al. 2020 ; Tarifa et al. 2009 ), noticeably expanding its distribution along the Bolivian eastern cordillera and encompassing two main phytogeographic regions: the Yungas and the Boliviano-Tucumano forest (Josse et al. 2011 ). Coming from Peru into central Bolivia, Yungas forests can be found in Cochabamba and part of Santa Cruz throughout a very broad altitude range (500-4,000 m), after which the southern portion of the eastern cordillera is dominated by Boliviano-Tucumano forests interspersed with Dry Interandean forests in the departments of Santa Cruz and Tarija. This transition occurs aFt about 18°S. With these new localities, A. boliviensis is now distributed across a mixed landscape of montane, dry, and subtropical forests (i.e., Yungas, Dry Interandean, and Tucumano forests) broken up by deep valleys and steep slopes (Graham et al. 2001 ; Rex and Hanratty 1989 ) (Fig. 1 ). While other species in the family are classified as either arboreal ( Cuscomys spp .) or rock specialists ( A. bennettii and the A. cinerea complex), field observations confirmed that A. boliviensis is found in Dry Interandean forests as well as montane and Polylepis forests -in some places still associated with rocky areas- both at ground level and on tree branches (Hidalgo-Cossio et al. 2016 ; Patton and Emmons 2015 ; Quiroga Pacheco et al. 2020 ). Since the 1950’s, Bolivian landscapes have been transformed by a variety of players (e.g., indigenous groups, colonists, and corporations) and practices (e.g., traditional agriculture, mechanized agriculture, cattle ranching or forest use), with most of the settlement and deforestation occurring around the city of Santa Cruz and the Yungas region near La Paz (Killeen et al. 2008 ). The land cover on the eastern cordillera has been converted into agricultural or bare land at the expense of the forests and pastures that covered this region in the past. This trend is particularly evident for the fertile valleys and moderate slopes below 2000 m that have been transformed for agricultural purposes (Brandt and Townsend 2006 ). And so, as new localities for A. boliviensis are found, the threats to its habitat remain the same (i.e., ecosystem conversion and degradation by human action), and new questions about population connectivity and genetic status of its lineages arise. Auspiciously, molecular data is available for samples in all these new localities, allowing us to evaluate, for the first time, the status of this unique and endangered rodent. Molecular approaches and population genetic analyses have become common practice in conservation studies. As a result, the field of conservation genetics identifies threats related to low density and small population sizes and provides tools to better understand demographically driven processes (Willi et al. 2022 ). It can also contribute to the management and conservation of species by identifying which populations should be the focus of these efforts. This can be achieved with the recognition of evolutionary significant units (ESUs). These units are attributed when populations are highly differentiated in their genetics and ecology, presumably because each unit is on a different evolutionary, and potentially adaptive, path (Funk et al. 2012 ; Willi et al. 2022 ). For example, population genetic studies of endangered or otherwise threatened taxa have found evidence for separate ESUs in mammals (Cossíos et al. 2012 ; Degner et al. 2007 ; Teixeira et al. 2023 ), birds (Murphy et al. 2011 ; Quintela et al. 2010 ), reptiles, and amphibians (Borges et al. 2018 ; Castillo-Morales et al. 2023 ; Muniz et al. 2018 ). It is also important to understand the genetic status of species because even when small-sized populations do not go extinct, they can still suffer genetic depletion to the point where their survival could be compromised in the face of environmental changes (O'Brien 1994 ). This phenomenon is especially relevant for species in naturally fragmented habitats (the case for A. boliviensis ) for which the effects of genetic drift could have a greater impact (Willi et al. 2022 ). Regarding A. boliviensis , the first challenge to overcome is the scarcity of information available. The species itself is rare and difficult to encounter (data exists on less than 20 individuals since its description in 1990). And even though recent findings are extremely valuable, data on the demography or abundance of its populations is nonexistent. Nonetheless, this study provides information on the distribution, genetic diversity, and structure of populations, as a first approximation to a comprehensive evaluation of the species. Moreover, because A. boliviensis is both endemic and endangered, understanding how its genetic diversity is apportioned will better inform future research, monitoring and ultimately, conservation efforts. MATERIALS AND METHODS Specimens examined Twenty-one specimens from eighteen localities spanning the known distribution of Abrocoma boliviensis and the northern half of the distribution of Abrocoma cinerea were used in the analyses, including 12 A . boliviensis from ten localities (including a topotypic specimen) and nine A. cinerea (Fig. 1 ; Table S1 ). Specimens were all procured via institutional loans from natural history collections. All tissue samples included in our analyses are accompanied by voucher specimens (Table S1 ). DNA extraction, PCR amplification, and sequencing of the cytochrome b gene Relationships among individuals of A. boliviensis were first evaluated using the cytochrome b ( Cyt-b) gene. For comparison, sequences of Abrocoma cinerea were also analyzed. Genomic DNA was extracted from muscle or liver tissue using the DNEasy® Blood and Tissue Kit from Qiagen and PCR amplifications followed the protocols used by Salazar-Bravo et al. ( 2023 ). One sample of A. boliviensis was a skin snippet that was first washed and hydrated following the protocol of de Moraes-Barros and Morgante ( 2007 ). The sample was subdivided so that one was processed with the Qiagen kit and the other with the Quick -DNA Fecal/Soil kit from Zymo Research. Skin extractions were then combined for subsequent PCR procedures. All of this took place in a laboratory that had not processed mammals before. PCR amplifications were viewed in 1% agarose gel, and successful amplifications were sent to Psomagen USA (Maryland) for sequencing. Single sequences were assembled into a contiguous sequence in DNASTAR v 5.52 and aligned and edited in the software Mesquite v3.61 (Maddison and Madisson 2019 ) using the MUSCLE option (Edgar 2004 ). The final matrix included 810bp of Cyt-b for 11 individuals of A. boliviensis and eight individuals of A. cinerea . Detailed information about the samples used and PCR cycling conditions is available in the supplementary information (Table S2). Analyses of Cytochrome b The Cyt-b gene tree was built with maximum likelihood (ML) searches implemented through the IQ-TREE web server (Trifinopoulos et al. 2016 ). Partition by codon was specified but allowing IQ-TREE to determine the best-fit substitution model for the data. Clade support was assessed via rapid bootstrapping with 1000 iterations. Trees were rooted at mid-point. Genetic variation at the inter- and intraspecific level was assessed via genetic distances calculated with the Kimura 2-parameter model as implemented in Mega 10 (Kumar et al. 2018 ), including distances between clades of A. boliviensis created based on the results the ML tree (see results section and Fig. 2 ). To further explore the genetic diversity of A. boliviensis , nucleotide diversity ( π ), haplotype number ( h ) and diversity ( Hd ), and polymorphic sites were calculated using DnaSP v6 (Rozas et al. 2017 ), and compared to values obtained for A. cinerea , which was the closest species with enough individuals available for these analyses. In addition, Fu’s Fs and Tajima’s D tests of neutrality were calculated in DnaSP to infer the historical demography of each species. Reduced-representation genome-level variation To determine whether the genetic distances and levels of nucleotide diversity obtained from Cyt-b are indeed representative of the evolutionary history of the clade and not biased by the unique characteristics of the mitochondrial genome (i.e., maternal inheritance), we explored the population structure of A. boliviensis and A. cinerea (for comparison) using a genome-wide reduced representation approach. A subset of the samples processed in this study were sequenced following the Genotyping-by-sequencing (GBS) methods described in Elshire et al. ( 2011 ). This included nine individuals of A. boliviensis and five individuals of A. cinerea for which DNA quality and content passed the requirements of the University of Wisconsin Biotechnology Center. The samples were digested with the enzymes nsiI and bfaI. The data received from the sequencing center was paired-end, but given the protocols utilized by the lab, there is a redundancy that could bias the analyses when using Stacks 2 (Rochette et al. 2019 ). Therefore, all downstream analyses were performed with the single-end dataset using the High Performance Computing Center at Texas Tech University. Loci filtering and SNP calling After demultiplexing and performing quality control checks on the reads, we ran several iterations of the denovo_map program in STACKS 2 (Rochette et al. 2019 ), modifying the parameters - m (which determines coverage depth in the ustacks module), - M (which determines the number of mismatches allowed to form a stack in the ustacks module) and - n (which controls the number of mismatches allowed between individuals when building the catalog in cstacks) to find the most appropriate combination to use in downstream analyses given our data. A detailed description of the process is given in the supplementary information. Most parameter combinations provided qualitatively similar results (i.e., the distribution and grouping of samples, as well as the amount of variance explained in the PCAs) at -r = 0.8. In VCFtools, the r80 files were further filtered using the options --max-meanDP 50 (to remove loci with high depth values), --minDP 7 (to ensure all genotypes were determined with at least 7 reads) and --thin 150 (to select only one SNP per locus). While it is common to perform allele filtering based on minimum allele frequency (in part to remove sequencing errors), we decided against it because this dataset has a low number of individuals, in some cases with only one individual representing a genetic cluster, and therefore any filtering based on MAF could potentially remove alleles that are biological relevant. Finally, for downstream analyses of both species, the files generated with the Stacks settings M = 4 and n = 5 were used because they provided the better balance between a high number of variants and avoiding combining paralogous or repetitive loci (Paris et al. 2017 ). Population analyses With the filtered dataset, PLINK was used to repeat the PCAs. This same VCF file was used as input to run the populations module of STACKS once more and generate a structure file. Population structure was assessed using the software structure v2.3.4 (Pritchard et al. 2000 ). Following the developer’s recommendations, the analysis was run assuming admixture is present (NOADMIX = 0), providing sampling location to assist the clustering (LOCPRIOR = 1), and specifying either an independent or a correlated allele frequency model (FREQSCORR = 0 and 1, respectively). Sampling location was provided as a prior because our dataset has both a low number of individuals and relatively small number of SNPs, which hinders the software’s capability to detect genuine population structure. Moreover, the model developed by (Hubisz et al. 2009 ) evaluates whether the sampling locations are informative, and only if they are, do they get used by the software. Using the command-line version of the program, the analysis was run for values of K from 1 to 5 ( A. boliviensis ) and 1 to 4 ( A. cinerea ). Each K value ran 20 times with BURNIN = 200 000 and NUMREPS = 500 000. To determine the most appropriate value of K for each species, we followed the Evanno method (Evanno et al. 2005 ) implemented through the STRUCTURE HARVESTER website (Earl and vonHoldt 2012 ) and the program Structure Plot v2.0 was used to generate the bar plots (Ramasamy et al. 2014 ). Next, to better understand the relationship between the population structure present in the species and its geography, estimated effective migrations surfaces (EEMS) were calculated to determine potential areas of higher-than-average and lower-than-average historic gene flow (Petkova et al. 2016 ). The surface plots were built using the C + + implementation for SNPS as described in the EMMS github page ( https://github.com/dipetkov/eems ). The outer file was built in ArcGIS Pro 3.1.0 using the sampling localities to create minimum convex polygon, for which a 100km buffer was added and then clipped to only include continental area. Using this polygon as input, the tools “Feature Vertices To Points” > “Add XY Coordinates” > “Export Feature Attribute To ASCII” were used in this order to obtain the sequence of vertices that outline a closed polygon. Three separate chains were run for each species with nDemes = 400, numMCMCIter = 5000000, numBurnIter = 1250000, and numThinIter = 1000. EEMS plots were created with the rEEMSplots package (Petkova 2023 ) implemented in R v4.0.5 (Team 2021 ). Finally, isolation by distance (IBD) was tested and plotted with the package adegenet (Jombart and Ahmed 2011 ) in R v4.0.5. RESULTS Cyt-b dataset The cytochrome b gene topology shows three clades which appear geographically structured from north to south (Fig. 2 a). Genetic distances among Cyt-b sequences in A. boliviensis are considerable, ranging from 0.3–13.9% (Table 1 ). The highest value within A. boliviensis (i.e., 13.9%) is still smaller than the distance registered between A. boliviensis and A. cinerea (17.4% on average) and intraspecific distance is larger in A. cinerea than in A. boliviensis . These levels of divergence support the classification of samples of A. boliviensis into three clades. Clade A includes all samples from Cochabamba, a sample from Potosi, and one sample from Santa Cruz (this represents the northern part of the distribution of the species in La Paz and the mesothermic valleys of central Cochabamba), clade B includes samples TK161712 + TK161714 + MNK3851 from the edge of the mountain rainforest in western most-Santa Cruz Department, and clade C (MHNC-M682) from the southern edge of the known distribution of the species in the Department of Tarija (in Fig. 2 a: yellow, purple and black stars, respectively). Table 1 Average interspecific genetics distances (%) within Abrocoma boliviensis calculated from cytochrome b sequences using Kimura 2-parameter. Abrocoma cinerea is included for comparison. Bold values on the diagonal represent intraspecific distances. 1 2 3 4 1 A. boliviensis (clade A) 1.3 2 A. boliviensis (clade B) 7.2 0.3 3 A. boliviensis (clade C) 13.9 12.1 - 4 A. cinerea 18.8 16.2 17.3 3.1 Values of nucleotide diversity and polymorphic sites are highly dependent on whether samples of A. boliviensis are grouped or not (Table 2 ). When all samples (regardless of clade) are pooled, nucleotide diversity is 0.045 and 121 polymorphic sites are present. For comparison, samples of A. cinerea (that span from southern Peru to northern Argentina) have a nucleotide diversity of 0.029 and 33 polymorphic sites. Once samples of boliviensis are grouped into clades, it becomes clear that the sample from clade C is inflating the values found for the whole species. When either clade A and B are considered separately, nucleotide diversity is reduced to 0.012 and 0.003, respectively while polymorphic sites are 28 and 4. And when clades A and B are combined, nucleotide diversity is 0.033 (slightly higher than in A. cinerea ) while polymorphic sites are more than double with 68 versus 33. Given that all samples of A. boliviensis come from separate localities, every individual (except for the two samples from Pusuq'huni) has a unique haplotype in both clades (A and B) which is reflected in their high haplotype diversity. Fs values were positive in all cases for A. boliviensis and only significant when clades A and B and B and C were combined. This value was negative and non-significant for A. cinerea . A similar trend was found for Tajima’s D values (Table 2 ). Table 2 Genetic diversity of A. boliviensis and A. cinerea based on cytochrome b sequences. Values for each clade and different groupings are also included. n: number of individuals included, h : number of haplotypes, Hd : haplotype diversity, Fs : Fu’s Fs statistic, and D : Tajima’s D statistic. * indicates statistically significant results. Species (n) Group Nucleotide diversity Polymorphic sites h Hd Fs D A. cinerea (8) All individuals 0.029 33 6 0.893 -0.76 -0.48 A. boliviensis (12) All individuals 0.045 121 10 0.917 1.57 0.23 A. boliviensis (11) clade A + B 0.033 68 9 0.905 2.0* 0.23 A. boliviensis (8) clade A 0.012 28 7 0.875 1.48 0.23 A. boliviensis (3) clade B 0.003 4 2 0.533 1.49 1.18 A. boliviensis (4) clade B + C 0.045 83 3 0.71 1.6* 0.7 Reduced-representation genome-level variation After demultiplexing and size selection (130bp) the number of retained reads was 253 725 812. After running denovo_map the number of loci and polymorphic loci shared by at least 80% of the individuals within A. boliviensis was 1 322 883 and 1 002 230, respectively. For A. cinerea it was 852 366 and 611 891. In addition, the number of variants retained after applying all filters in VCFtools was 2192 for A. boliviensis and 2850 for A. cinerea . The PCA plot built from GBS data supports the groups formed in the Cyt-b tree (Fig. 2 a, 2 b), with the PC1 explaining 51.9% of the variance between the specimen from clade C (Tarija) and the rest of the samples and the PC2 explaining 20.4% of the variance found in the specimen from clade B (Santa Cruz). Because their distance in multivariate space is so marked, we could not assess any structure within clade A unless the other two clades were removed. By doing so, it became apparent that, as described by the Cyt-b tree, the sample from Sailapata is distanced from the rest and it explains 28.8% of the variation. The remaining samples show virtually no separation on the horizontal axis, but there is differentiation seen on the y-axis that explains 20.6% of the variation. The distribution of these samples seems to be determined by distance in a somewhat east to west fashion, from the eastern most sample in Sach’a Loma going towards the other side of the Rocha-Caine-Grande River system to the western samples from Pusuq’huni and Torotoro. To serve as a reference, the five samples of A. cinerea that come from southern Peru, Bolivia, and northern Argentina do not display prominent structure other than the samples from northern Argentina and southern Bolivia (from the Reserva Nacional de Fauna Andina Eduardo Avaroa [RNFA Eduardo Avaroa]) grouping together, somewhat distanced from the other two (Fig. 2 d, 2 e). Here, the PC1 explains 39.1% of the variation and the PC2 explains 29.3%. Following the Evanno method, population structure in A. boliviensis is better explained at K = 2 when independent allele frequencies are used (Mean LnP = -23,790, SD = 5.11, and ΔK = 753.28). In this case, one population consists of all samples from clade A (with inferred ancestry of 1) and another population represents the sample from clade C (with inferred ancestry of 1). The sample from clade B shows admixture with most ancestry shared with clade A (0.658) and less with clade C (0.342) (Fig. 2 c). Under a correlated allele frequency model, K = 3 is selected as the best partition (Mean LnP = -18,837, SD = 601.5, and ΔK = 8.76) with a third cluster being assigned to samples CBF5455 (ancestry of 0.382) and TK161718 (ancestry of 0.572). Given the larger values of LnP and SD and the much smaller ΔK, this assignment of K = 3 could be an artifact of the model which has a risk of over-estimating K. Finally, analyses for A. cinerea were not able to detect any population structure within the samples, which is most likely due to the low number of samples included (n = 5). The EEMS plot of A. boliviensis shows two areas of lower-than-average gene flow, one between samples from clade A and B in southern Cochabamba and the other one surrounding the sample from clade C in Tarija. Both areas have low intensity (lightest shade of orange). One area of higher-than-average gene flow is portrayed in the mesothermic valleys of central Cochabamba where most of clade A comes from. In the case of A. cinerea , the low number of samples and their dispersal on the landscape generated low intensity areas of higher-than and lower-than-average gene flow that are located where the samples are (Fig. 3 ). Tests of isolation by distance suggest that the structure seen in A. boliviensis might be explained by other biological scenarios. The histograms in Fig. 3 show that the original value of the correlation between distance matrices (the black dot) falls inside the reference distribution (the histogram itself) indicating that isolation by distance is not significant (Fig. 3 ). DISCUSSION Analyses of both cytochrome b and genomic reduced representation make it clear that samples of Abrocoma boliviensis evaluated in this study represent three separate clades, as evidenced by the high values of genetic distance (Table 1 ), little to no admixture (Fig. 2 ), and long divergence times between lineages (Arenas-Viveros et al. In prep). In addition, the highly fragmented and transformed landscape, which might be imposing higher isolation, places each of these clades and the species as a whole at a higher risk of extinction. The genetic structuring found within A. boliviensis, namely clades A, B and C, is evident in both the Cyt-b gene tree and the PCA analyses performed with GBS data (Fig. 2 ). This same structure was supported, to an extent, by the STRUCTURE analysis where clades A and C form their own cluster with no shared ancestry between them, and clade B (or sample MNK3851) shares ancestry with both clades, with a higher percentage being shared with members of clade A. In addition, we found that these genetic clusters are geographically and ecologically structured. Clades A and C occur in different ecoregions (Yungas or Dry Interandean dominated region vs. mosaic of Boliviano-Tucumano and Dry Interandean forests) and divergence time estimates between the three clades are in the order of millions of years (Arenas-Viveros et al. In prep), suggesting that these lineages have been on separate evolutionary pathways and should therefore be identified, at minimum, as separate ESUs (Evolutionary Significant Units). A case could be made to elevate each clade to the hierarchy of subspecies or alternatively, to elevate clades A and B to subspecies level and assign the individual from southern Bolivia (i.e., Clade C) to a possible new species, considering the high levels of genetic distance between clades (7–13%), habitat differences, and divergence times. However, we prefer to take a much more reserved position in the understanding that genetic divergences by themselves are not taxonomic characters. Moreover, comparative studies on the morphology and ecology of members of each clade are lacking, and until that piece of the puzzle is added, it would be difficult to draw well-supported conclusions on the taxonomy of the species. For instance, another Andean highland specialist, the vicuna ( Vicugna vicugna ), is divided into subspecies from the Wet and Dry Puna respectively, and this taxonomic arrangement is supported by morphological and genetic traits (Marín et al. 2007 ). Not to mention that it is necessary to add more data from clades B and C and confirm whether the lack of samples from the department of Chuquisaca (i.e., the area between clades B and C along the eastern Andes) is biologically significant or more of a sampling artifact. For example, the distribution of the Andean cat Leopardus jacobita , is considered naturally fragmented due to its preference for high altitude environments and the fact that no individuals have been recorded between 30 and 35°S (Cossíos et al. 2012 ). Regardless, the potential for a taxonomic rearrangement is there, and in fact similar studies on rare and/or endangered species have revealed cryptic diversity that support the assignment of new species or confirmed the existence of subspecies (Castillo-Morales et al. 2023 ; Degner et al. 2007 ; Murphy et al. 2011 ). Levels of nucleotide diversity ( π ) and number of polymorphic sites in the Bolivian chinchilla rat are undoubtedly affected by whether ESUs are grouped or evaluated individually. Just including the specimen from Tarija (i.e., clade C) doubles the number of polymorphic sites (121 vs 68) and adds 36% more nucleotide diversity (0.045 vs 0.033) than when not included (Table 2 ). Compared to values of its sister clade Abrocoma cinerea ( π = 0.029 and 33 polymorphic sites), nucleotide diversity is similar only when Clades A&B are combined, but the number of polymorphic sites is still double for A. boliviensis . This is interesting because the samples of A. cinerea used in this study cover a large geographic area through the Altiplano, from southern Peru to northern Argentina (~ 1,015 km linear distance). Meanwhile, the northern most sample of A. boliviensis (from Sailapata in Cochabamba) is about 527 km (linear distance) from the sample collected in Tarija and only ~ 340 km from Clade B in Santa Cruz. This fact highlights how more genetic diversity and polymorphisms are contained in a species whose distribution is restricted to one slope of the Andes and one country, than in one spread along a much larger area in the continent. In contrast, when values were calculated for clades A and B separately, nucleotide diversity drops significantly ( π = 0.012 and 0.003 respectively) while polymorphic sites reach levels like those of A. cinerea for Clade A (28) and are remarkably fewer than in clade B (4). This comparison indicates how each ESU carries unique diversity that is not found in other populations, and reinforces the hypothesis that any impact on one of these lineages might result in drastic genetic loss for the species; furthering their case for separate efforts in their monitoring, research, and conservation (Borges et al. 2018 ). Values of Fu’s Fs and Tajima’s D are all positive for A. boliviensis regardless of grouping arrangement and only significant for Fs when clades A + B and B + C are evaluated, suggesting a scenario of population bottleneck or the effect of balancing selection. Values for A. cinerea on the contrary, are negative but non-significant for both estimates, indicating possible scenarios of population expansion. Similar results were obtained by Castillo-Morales et al. ( 2023 ), in which two newly discovered lineages of the endangered leatherback turtle ( Dermochelys coriacea ) had positive values for both Fs and D , while samples of the less threatened olive ridley turtles ( Lepidochelys olivacea ) had negative values for the same estimators. In their study, these results coincided with what was already known about these species: populations of leatherback turtles are declining while those of L. olivacea have been increasing in the last decades. Unfortunately, there are no long-term estimates of population trends in any species of abrocomid, but the results of our study inform and provide an approximation to the status of these populations. Taken at face value, our results suggest that populations of Abrocoma boliviensis may have gone through genetic bottlenecks which, in combination with the small distribution range of the species, warrants further conservation investments. The evolutionary history of extant abrocomids traces its origins to about 10Ma, when the Andes and its surrounding lowlands where still experiencing orogenic changes that in turn transformed the landscape and promoted divergence (Garzione et al. 2008 ; Graham 2009 ; Gregory-Wodzicki 2000 ). The ancestor of A. boliviensis was on its own evolutionary pathway shortly before 6 Ma when the eastern cordillera experienced a second uplift (Arenas-Viveros et al. In prep). At this point, the eastern slope of the Andes has had its characteristic corrugated topology since its first uplifted about 10 Ma, but as the mountain chain kept elevating, this jagged landscape created barriers for the movement of taxa at mid and now also, high elevations (Graham 2009 ). Moreover, palaeobotanical data provides evidence for the expansion of the flora typical of the northern region (i.e., Yungas forests) along with the uplift (Graham et al. 2001 ), while Seasonally Dry Tropical Forests which had a continuous distribution from the Andean foothills to the Caatingas in Brazil during the upper Pleistocene, became disjunct and fragmented by other habitat types (e.g., Tucumano-Boliviano forests in the southern distribution of A. boliviensis) as a result of dry-cold vs humid-warm climatic cycles (Mogni et al. 2015). These historical barriers to migration along with adaptations to different environments (i.e., the different ecoregions inhabited by northern and southern samples) and modern-day transformation of the landscape, could explain the levels of divergence and the little to no of admixture found between lineages. As mentioned before, the area where the Bolivian chinchilla rat is distributed is one of the most transformed and exploited by humans in the country (Brandt and Townsend 2006 ; Killeen et al. 2008 ). This creates another source of pressure for the species, because even if some of its populations are doing well, this might not be the case as the environment changes in the future. Compared to the Andean cat, another species of endangered mammal (Cossíos et al. 2012 ) and the Degu Octodontomys gliroides , another rodent from the Altiplano (Rivera et al. 2016 ), haplotype and nucleotide diversity are both higher in A. boliviensis regardless of ESU grouping. Nevertheless, these ESUs have been isolated from one another by past processes and continue to be by modern transformation and degradation of the landscape. One of the main purposes of identifying and conserving ESUs is to maintain genetic diversity that will maximize the evolutionary and adaptive potential of a species in the face of environmental change (Funk et al. 2012 ). This is of special importance in the eastern Andes because in addition to human-driven transformation, studies have shown that vegetation shifts in the past (mostly of Polylepis woodlands within the Andean grasslands) have been mediated by variations in temperature, precipitation and burning regime (Williams et al. 2011 ), all of which are incremented by climate change. Abrocoma boliviensis is known to be associated with forested areas, and unlike other abrocomids in the Altiplano that inhabit more open environments (Patton and Emmons 2015 ), these changes to the landscape threaten the future of its populations unless intervention for the management and conservation of the native landscape takes place. In addition to being endangered and endemic, the Bolivian chinchilla rat is also rare, but this might present an advantage in at least one aspect of its conservation. When evaluating areas to potentially designate as protected, using one taxonomic group as a proxy to community diversity might not be the best approach because of the low congruence between geographic patterns of diversity across taxonomic groups (e.g., vertebrate classes or terrestrial vs. aquatic organisms). On the other hand, while rare species are the most likely to be lost if they are not protected, they are one of the best indicators when selecting sites with the aim of preserving diversity (Lawler et al. 2003 ). In fact, protected areas that have been delineated with the purpose of conserving rare species have been shown to protect biodiversity more generally (Drever et al. 2012 ). Moreover, the presence and maintenance of rare species contributes to, and supports, the functional structure of assemblages and ecosystems (Leitão et al. 2016 ; Mouillot et al. 2013 ). All of this, along with the potential to assign an economic value to the conservation of A. boliviensis (e.g., from ecotourism and conservation donations for a charismatic species), might serve to persuade the appropriate authorities to provide the support needed to launch systematic monitoring and research programs (Drever et al. 2012 ). Lastly, based on the results presented herein and considering how much is still not known of A. boliviensis , this study highlights the urgency with which survey efforts must become the first order of action. Once more population-level data becomes available, demographic, ecological, and genetic studies should provide a better understanding of the species and the evolutionary trajectory of its lineages, including whether a taxonomic rearrangement is needed. This in turn, will better inform any management and conservation actions, including areas for monitoring, creation of protected areas and corridors, as well as mitigation plans to reduce the effects of anthropogenic transformation of the landscape. Declarations Funding Author DAV has received research support from The Graduate School and Biological Sciences Department, including the J Knox Jone and the Fonseca Mammal Scholarships, at Texas Tech University. JSB acknowledges funding from The Mohamed Bin Zayed Species Conservation Fund, The Texas Tech University Scholarship Catalyst Program and the Fulbright US Scholars Program. Competing interests The authors have no relevant financial or non-financial interests to disclose Author Contribution D.A.V and J.S.B concieved and designed the study, D.A.V analyzed data, interpreted results, and wrote the main manuscript text, T.T. and X.V.L provided critical revisions. All authors contributed materials and reviewed the manuscript. Acknowledgement The following individuals and institutions were instrumental in providing loan material or facilitating access to their collections, without which this study would not have been possible: Julieta Vargas (formerly at Colección Boliviana de Fauna, Museo Nacional de Historia Natural de Bolivia), Freddy Navarro, Luis Aguirre, Renzo Vargas (Centro de Biodiversidad y Genética, Cochabamba, Bolivia), Kathia Rivero, Luis Acosta (Colección de Mamíferos, Museo de Historia Natural "Noel Kempff Mercado", Santa Cruz, Bolivia), Ricardo Céspedes (Museo de Historia Natural “Alcide d'Orbigny”, Cochabamba, Bolivia), Robert Bradley, Heath Garner (Natural Science Research Laboratory, Texas Tech University), Jim Patton, Chris Conroy (Museum of Vertebrate Zoology, University of California), Michael Mares, Janet Braun (Sam Noble Museum of Natural History, The University of Oklahoma), Evaristo Lopez, Cesar Medina (Museo de la Universidad Nacional San Agustín de Arequipa, Perú). Further, we would like to thank the following colleagues for their efforts in securing specimens in the field: Carola Azurduy, Nuria Bernal Hoverud, Jasper K. Rasmussen, Oliver Quinteros, and Fray Andrés Langer (deceased). We thank the Andean Carnivore Conservation Program for their efforts in obtaining a unique sample at the southern edge of the known distribution of A. boliviensis. Data Availability Data is provided within the manuscript or supplementary information files. Sequence data will be deposited in GenBank. References Bernal N (2016) Abrocoma boliviensis. The IUCN Red List of Threatened Species 2016. https://dx.doi.org/10.2305/IUCN.UK.2016-2.RLTS.T18A22182349.en. Borges VS, Santiago PC, Lima NGS, Coutinho ME, Eterovick PC, Carvalho DC (2018) Evolutionary Significant Units within Populations of Neotropical Broad-Snouted Caimans (Caiman latirostris, Daudin, 1802). 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Supplementary Files ArenasViverosetal.SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 02 May, 2026 Read the published version in Conservation Genetics → Version 1 posted Editorial decision: Revision requested 01 Nov, 2025 Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviews received at journal 27 Jun, 2025 Reviewers agreed at journal 29 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 25 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 23 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-6513653","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450752587,"identity":"7b9f6661-f998-4226-8f9f-5e53d5dc20ca","order_by":0,"name":"Daniela Arenas-Viveros","email":"data:image/png;base64,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","orcid":"","institution":"Texas Tech University","correspondingAuthor":true,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Arenas-Viveros","suffix":""},{"id":450752588,"identity":"3a381037-4f16-4b9b-9493-c5dc4ecaca0c","order_by":1,"name":"Teresa Tarifa","email":"","orcid":"","institution":"The College of Idaho","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"Tarifa","suffix":""},{"id":450752589,"identity":"45a04540-bcd2-43ac-a70c-b6a059c2b898","order_by":2,"name":"Marisol Hidalgo-Cossio","email":"","orcid":"","institution":"Colección Boliviana de Fauna, Museo de Historia Natural","correspondingAuthor":false,"prefix":"","firstName":"Marisol","middleName":"","lastName":"Hidalgo-Cossio","suffix":""},{"id":450752590,"identity":"3611c3a6-155d-4773-92e3-8134aedc0f96","order_by":3,"name":"Omar F. 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While the southernmost record of A. boliviensis in the state of Tarija appears to be from Tucumano-Boliviano forests, so far in the area, the species has been recorded in patches of Dry Interandean forest.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6513653/v1/250982316d62988df968c8f8.png"},{"id":81954100,"identity":"8445557b-15c8-4385-890f-ccce8a503bf9","added_by":"auto","created_at":"2025-05-05 09:41:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":341303,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure of \u003cem\u003eA. boliviensis\u003c/em\u003e. \u003cstrong\u003ea:\u003c/strong\u003e \u003cem\u003eCyt-b\u003c/em\u003e tree with corresponding localities on the map to the right (bolded catalog numbers represent individuals for which GBS was performed); \u003cstrong\u003eb:\u003c/strong\u003e PCA plots built from GBS data, the panel on the right only includes members of clade A. \u003cstrong\u003ec:\u003c/strong\u003e \u003cem\u003estructure\u003c/em\u003e bar plot. For comparison, \u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ee\u003c/strong\u003e are the same analyses as a and b but for \u003cem\u003eA. cinerea\u003c/em\u003e (\u003cem\u003estructure\u003c/em\u003e analyses did not provide a definitive K value for this group)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6513653/v1/8705abda8f0f68bf6cdee3ef.png"},{"id":81954096,"identity":"421b7870-b1e0-460d-920b-17672a727887","added_by":"auto","created_at":"2025-05-05 09:41:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142329,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated migration surface and isolation by distance plots for both \u003cem\u003eA. boliviensis\u003c/em\u003e and \u003cem\u003eA. cinerea\u003c/em\u003e. Areas of lower-than-average gene flow are depicted in shades of orange while areas of higher-than-average gene flow are depicted in shades of blue. In the histograms, the original value of the correlation between distance matrices is shown by the black dot, compared to the reference distribution\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6513653/v1/7258250b73034ff20b6caa0a.png"},{"id":108809527,"identity":"8888f1d3-49b1-401e-b7de-509b496e4d06","added_by":"auto","created_at":"2026-05-08 15:53:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1150801,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6513653/v1/0af5d302-53fd-4c7b-9036-cb53b346c77c.pdf"},{"id":81954095,"identity":"b04a2e09-542d-4c59-a54f-4c2407765c6d","added_by":"auto","created_at":"2025-05-05 09:41:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26951,"visible":true,"origin":"","legend":"","description":"","filename":"ArenasViverosetal.SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6513653/v1/0132eabf138aab9838ddf66d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Population genetics and lineage structure of the endangered Bolivian chinchilla rat Abrocoma boliviensis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe Bolivian chinchilla rat (\u003cem\u003eAbrocoma boliviensis\u003c/em\u003e) is a rare and endemic species, found only in mid-to-high elevation forests of the eastern Andes in Bolivia (Hidalgo-Cossio et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Quinteros-Mu\u0026ntilde;oz \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Quiroga Pacheco et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tarifa et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Rarity in a species can be determined based on abundance or restricted geography, and while not all rare species are necessarily at the risk of extinction, their low abundance and/or constrained distributions do make them vulnerable to stochastic events (Drever et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the latest assessment of \u003cem\u003eA. boliviensis\u003c/em\u003e for the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, the distribution of \u003cem\u003eA. boliviensis\u003c/em\u003e is limited to the type locality in the department of Santa Cruz and is classified as critically endangered under criteria B1ab(i,ii,ii) (Bernal \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This means that the species\u0026rsquo; range extent is smaller than 100 km2, severely fragmented or known to exist at a single location, and its habitat presents a continuing decline in area, extent and/or quality. Further, the species has been recognized as one of the 100 EDGE mammal species in the world, a list that comprises species recognized as evolutionarily highly distinct and threatened with extinction (Gumbs et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, sampling efforts have added at least eight new localities in the departments of Potos\u0026iacute;, Cochabamba, Tarija, and Santa Cruz (Hidalgo-Cossio et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Quinteros-Mu\u0026ntilde;oz \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Quiroga Pacheco et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tarifa et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), noticeably expanding its distribution along the Bolivian eastern cordillera and encompassing two main phytogeographic regions: the Yungas and the Boliviano-Tucumano forest (Josse et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Coming from Peru into central Bolivia, Yungas forests can be found in Cochabamba and part of Santa Cruz throughout a very broad altitude range (500-4,000 m), after which the southern portion of the eastern cordillera is dominated by Boliviano-Tucumano forests interspersed with Dry Interandean forests in the departments of Santa Cruz and Tarija. This transition occurs aFt about 18\u0026deg;S. With these new localities, \u003cem\u003eA. boliviensis\u003c/em\u003e is now distributed across a mixed landscape of montane, dry, and subtropical forests (i.e., Yungas, Dry Interandean, and Tucumano forests) broken up by deep valleys and steep slopes (Graham et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rex and Hanratty \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While other species in the family are classified as either arboreal (\u003cem\u003eCuscomys spp\u003c/em\u003e.) or rock specialists (\u003cem\u003eA. bennettii\u003c/em\u003e and the \u003cem\u003eA. cinerea\u003c/em\u003e complex), field observations confirmed that \u003cem\u003eA. boliviensis\u003c/em\u003e is found in Dry Interandean forests as well as montane and \u003cem\u003ePolylepis\u003c/em\u003e forests -in some places still associated with rocky areas- both at ground level and on tree branches (Hidalgo-Cossio et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Patton and Emmons \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Quiroga Pacheco et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the 1950\u0026rsquo;s, Bolivian landscapes have been transformed by a variety of players (e.g., indigenous groups, colonists, and corporations) and practices (e.g., traditional agriculture, mechanized agriculture, cattle ranching or forest use), with most of the settlement and deforestation occurring around the city of Santa Cruz and the Yungas region near La Paz (Killeen et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The land cover on the eastern cordillera has been converted into agricultural or bare land at the expense of the forests and pastures that covered this region in the past. This trend is particularly evident for the fertile valleys and moderate slopes below 2000 m that have been transformed for agricultural purposes (Brandt and Townsend \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). And so, as new localities for \u003cem\u003eA. boliviensis\u003c/em\u003e are found, the threats to its habitat remain the same (i.e., ecosystem conversion and degradation by human action), and new questions about population connectivity and genetic status of its lineages arise. Auspiciously, molecular data is available for samples in all these new localities, allowing us to evaluate, for the first time, the status of this unique and endangered rodent.\u003c/p\u003e \u003cp\u003eMolecular approaches and population genetic analyses have become common practice in conservation studies. As a result, the field of conservation genetics identifies threats related to low density and small population sizes and provides tools to better understand demographically driven processes (Willi et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It can also contribute to the management and conservation of species by identifying which populations should be the focus of these efforts. This can be achieved with the recognition of evolutionary significant units (ESUs). These units are attributed when populations are highly differentiated in their genetics and ecology, presumably because each unit is on a different evolutionary, and potentially adaptive, path (Funk et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Willi et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, population genetic studies of endangered or otherwise threatened taxa have found evidence for separate ESUs in mammals (Coss\u0026iacute;os et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Degner et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Teixeira et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), birds (Murphy et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Quintela et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), reptiles, and amphibians (Borges et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Castillo-Morales et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Muniz et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is also important to understand the genetic status of species because even when small-sized populations do not go extinct, they can still suffer genetic depletion to the point where their survival could be compromised in the face of environmental changes (O'Brien \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This phenomenon is especially relevant for species in naturally fragmented habitats (the case for \u003cem\u003eA. boliviensis\u003c/em\u003e) for which the effects of genetic drift could have a greater impact (Willi et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding \u003cem\u003eA. boliviensis\u003c/em\u003e, the first challenge to overcome is the scarcity of information available. The species itself is rare and difficult to encounter (data exists on less than 20 individuals since its description in 1990). And even though recent findings are extremely valuable, data on the demography or abundance of its populations is nonexistent. Nonetheless, this study provides information on the distribution, genetic diversity, and structure of populations, as a first approximation to a comprehensive evaluation of the species. Moreover, because \u003cem\u003eA. boliviensis\u003c/em\u003e is both endemic and endangered, understanding how its genetic diversity is apportioned will better inform future research, monitoring and ultimately, conservation efforts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eSpecimens examined\u003c/p\u003e \u003cp\u003eTwenty-one specimens from eighteen localities spanning the known distribution of \u003cem\u003eAbrocoma boliviensis\u003c/em\u003e and the northern half of the distribution of \u003cem\u003eAbrocoma cinerea\u003c/em\u003e were used in the analyses, including 12 \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eboliviensis\u003c/em\u003e from ten localities (including a topotypic specimen) and nine \u003cem\u003eA. cinerea\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Specimens were all procured via institutional loans from natural history collections. All tissue samples included in our analyses are accompanied by voucher specimens (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDNA extraction, PCR amplification, and sequencing of the cytochrome b gene\u003c/p\u003e \u003cp\u003eRelationships among individuals of \u003cem\u003eA. boliviensis\u003c/em\u003e were first evaluated using the cytochrome b (\u003cem\u003eCyt-b)\u003c/em\u003e gene. For comparison, sequences of \u003cem\u003eAbrocoma cinerea\u003c/em\u003e were also analyzed. Genomic DNA was extracted from muscle or liver tissue using the DNEasy\u0026reg; Blood and Tissue Kit from Qiagen and PCR amplifications followed the protocols used by Salazar-Bravo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). One sample of \u003cem\u003eA. boliviensis\u003c/em\u003e was a skin snippet that was first washed and hydrated following the protocol of de Moraes-Barros and Morgante (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The sample was subdivided so that one was processed with the Qiagen kit and the other with the \u003cem\u003eQuick\u003c/em\u003e-DNA Fecal/Soil kit from Zymo Research. Skin extractions were then combined for subsequent PCR procedures. All of this took place in a laboratory that had not processed mammals before. PCR amplifications were viewed in 1% agarose gel, and successful amplifications were sent to Psomagen USA (Maryland) for sequencing. Single sequences were assembled into a contiguous sequence in DNASTAR v 5.52 and aligned and edited in the software Mesquite v3.61 (Maddison and Madisson \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) using the MUSCLE option (Edgar \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The final matrix included 810bp of \u003cem\u003eCyt-b\u003c/em\u003e for 11 individuals of \u003cem\u003eA. boliviensis\u003c/em\u003e and eight individuals of \u003cem\u003eA. cinerea\u003c/em\u003e. Detailed information about the samples used and PCR cycling conditions is available in the supplementary information (Table S2).\u003c/p\u003e \u003cp\u003eAnalyses of Cytochrome b\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eCyt-b\u003c/em\u003e gene tree was built with maximum likelihood (ML) searches implemented through the IQ-TREE web server (Trifinopoulos et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Partition by codon was specified but allowing IQ-TREE to determine the best-fit substitution model for the data. Clade support was assessed via rapid bootstrapping with 1000 iterations. Trees were rooted at mid-point. Genetic variation at the inter- and intraspecific level was assessed via genetic distances calculated with the Kimura 2-parameter model as implemented in Mega 10 (Kumar et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), including distances between clades of \u003cem\u003eA. boliviensis\u003c/em\u003e created based on the results the ML tree (see results section and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo further explore the genetic diversity of \u003cem\u003eA. boliviensis\u003c/em\u003e, nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), haplotype number (\u003cem\u003eh\u003c/em\u003e) and diversity (\u003cem\u003eHd\u003c/em\u003e), and polymorphic sites were calculated using DnaSP v6 (Rozas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and compared to values obtained for \u003cem\u003eA. cinerea\u003c/em\u003e, which was the closest species with enough individuals available for these analyses. In addition, Fu\u0026rsquo;s \u003cem\u003eFs\u003c/em\u003e and Tajima\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e tests of neutrality were calculated in DnaSP to infer the historical demography of each species.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eReduced-representation genome-level variation\u003c/p\u003e\u003cp\u003eTo determine whether the genetic distances and levels of nucleotide diversity obtained from \u003cem\u003eCyt-b\u003c/em\u003e are indeed representative of the evolutionary history of the clade and not biased by the unique characteristics of the mitochondrial genome (i.e., maternal inheritance), we explored the population structure of \u003cem\u003eA. boliviensis\u003c/em\u003e and \u003cem\u003eA. cinerea\u003c/em\u003e (for comparison) using a genome-wide reduced representation approach. A subset of the samples processed in this study were sequenced following the Genotyping-by-sequencing (GBS) methods described in Elshire et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This included nine individuals of \u003cem\u003eA. boliviensis\u003c/em\u003e and five individuals of \u003cem\u003eA. cinerea\u003c/em\u003e for which DNA quality and content passed the requirements of the University of Wisconsin Biotechnology Center. The samples were digested with the enzymes nsiI and bfaI. The data received from the sequencing center was paired-end, but given the protocols utilized by the lab, there is a redundancy that could bias the analyses when using Stacks 2 (Rochette et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, all downstream analyses were performed with the single-end dataset using the High Performance Computing Center at Texas Tech University.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eLoci filtering and SNP calling\u003c/p\u003e \u003cp\u003eAfter demultiplexing and performing quality control checks on the reads, we ran several iterations of the \u003cem\u003edenovo_map\u003c/em\u003e program in STACKS 2 (Rochette et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), modifying the parameters -\u003cb\u003em\u003c/b\u003e (which determines coverage depth in the ustacks module), -\u003cb\u003eM\u003c/b\u003e (which determines the number of mismatches allowed to form a stack in the ustacks module) and -\u003cb\u003en\u003c/b\u003e (which controls the number of mismatches allowed between individuals when building the catalog in cstacks) to find the most appropriate combination to use in downstream analyses given our data. A detailed description of the process is given in the supplementary information.\u003c/p\u003e \u003cp\u003eMost parameter combinations provided qualitatively similar results (i.e., the distribution and grouping of samples, as well as the amount of variance explained in the PCAs) at -r\u0026thinsp;=\u0026thinsp;0.8. In VCFtools, the r80 files were further filtered using the options --max-meanDP 50 (to remove loci with high depth values), --minDP 7 (to ensure all genotypes were determined with at least 7 reads) and --thin 150 (to select only one SNP per locus). While it is common to perform allele filtering based on minimum allele frequency (in part to remove sequencing errors), we decided against it because this dataset has a low number of individuals, in some cases with only one individual representing a genetic cluster, and therefore any filtering based on MAF could potentially remove alleles that are biological relevant. Finally, for downstream analyses of both species, the files generated with the Stacks settings \u003cb\u003eM\u0026thinsp;=\u0026thinsp;4\u003c/b\u003e and \u003cb\u003en\u0026thinsp;=\u0026thinsp;5\u003c/b\u003e were used because they provided the better balance between a high number of variants and avoiding combining paralogous or repetitive loci (Paris et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePopulation analyses\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWith the filtered dataset, PLINK was used to repeat the PCAs. This same VCF file was used as input to run the \u003cem\u003epopulations\u003c/em\u003e module of STACKS once more and generate a structure file. Population structure was assessed using the software structure v2.3.4 (Pritchard et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Following the developer\u0026rsquo;s recommendations, the analysis was run assuming admixture is present (NOADMIX\u0026thinsp;=\u0026thinsp;0), providing sampling location to assist the clustering (LOCPRIOR\u0026thinsp;=\u0026thinsp;1), and specifying either an independent or a correlated allele frequency model (FREQSCORR\u0026thinsp;=\u0026thinsp;0 and 1, respectively). Sampling location was provided as a prior because our dataset has both a low number of individuals and relatively small number of SNPs, which hinders the software\u0026rsquo;s capability to detect genuine population structure. Moreover, the model developed by (Hubisz et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) evaluates whether the sampling locations are informative, and only if they are, do they get used by the software.\u003c/p\u003e\u003cp\u003eUsing the command-line version of the program, the analysis was run for values of K from 1 to 5 (\u003cem\u003eA. boliviensis\u003c/em\u003e) and 1 to 4 (\u003cem\u003eA. cinerea\u003c/em\u003e). Each K value ran 20 times with BURNIN\u0026thinsp;=\u0026thinsp;200 000 and NUMREPS\u0026thinsp;=\u0026thinsp;500 000. To determine the most appropriate value of K for each species, we followed the Evanno method (Evanno et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) implemented through the STRUCTURE HARVESTER website (Earl and vonHoldt \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the program Structure Plot v2.0 was used to generate the bar plots (Ramasamy et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNext, to better understand the relationship between the population structure present in the species and its geography, estimated effective migrations surfaces (EEMS) were calculated to determine potential areas of higher-than-average and lower-than-average historic gene flow (Petkova et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The surface plots were built using the C\u0026thinsp;+\u0026thinsp;+\u0026thinsp;implementation for SNPS as described in the EMMS github page (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dipetkov/eems\u003c/span\u003e\u003cspan address=\"https://github.com/dipetkov/eems\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The outer file was built in ArcGIS Pro 3.1.0 using the sampling localities to create minimum convex polygon, for which a 100km buffer was added and then clipped to only include continental area. Using this polygon as input, the tools \u0026ldquo;Feature Vertices To Points\u0026rdquo; \u0026gt; \u0026ldquo;Add XY Coordinates\u0026rdquo; \u0026gt; \u0026ldquo;Export Feature Attribute To ASCII\u0026rdquo; were used in this order to obtain the sequence of vertices that outline a closed polygon. Three separate chains were run for each species with nDemes\u0026thinsp;=\u0026thinsp;400, numMCMCIter\u0026thinsp;=\u0026thinsp;5000000, numBurnIter\u0026thinsp;=\u0026thinsp;1250000, and numThinIter\u0026thinsp;=\u0026thinsp;1000. EEMS plots were created with the rEEMSplots package (Petkova \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) implemented in R v4.0.5 (Team \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, isolation by distance (IBD) was tested and plotted with the package adegenet (Jombart and Ahmed \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in R v4.0.5.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cem\u003eCyt-b\u003c/em\u003e dataset\u003c/p\u003e \u003cp\u003eThe cytochrome b gene topology shows three clades which appear geographically structured from north to south (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Genetic distances among \u003cem\u003eCyt-b\u003c/em\u003e sequences in \u003cem\u003eA. boliviensis\u003c/em\u003e are considerable, ranging from 0.3\u0026ndash;13.9% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The highest value within \u003cem\u003eA. boliviensis\u003c/em\u003e (i.e., 13.9%) is still smaller than the distance registered between \u003cem\u003eA. boliviensis\u003c/em\u003e and \u003cem\u003eA. cinerea\u003c/em\u003e (17.4% on average) and intraspecific distance is larger in \u003cem\u003eA. cinerea\u003c/em\u003e than in \u003cem\u003eA. boliviensis\u003c/em\u003e. These levels of divergence support the classification of samples of \u003cem\u003eA. boliviensis\u003c/em\u003e into three clades. Clade A includes all samples from Cochabamba, a sample from Potosi, and one sample from Santa Cruz (this represents the northern part of the distribution of the species in La Paz and the mesothermic valleys of central Cochabamba), clade B includes samples TK161712\u0026thinsp;+\u0026thinsp;TK161714\u0026thinsp;+\u0026thinsp;MNK3851 from the edge of the mountain rainforest in western most-Santa Cruz Department, and clade C (MHNC-M682) from the southern edge of the known distribution of the species in the Department of Tarija (in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea: yellow, purple and black stars, respectively).\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\u003eAverage interspecific genetics distances (%) within \u003cem\u003eAbrocoma boliviensis\u003c/em\u003e calculated from cytochrome b sequences using Kimura 2-parameter. \u003cem\u003eAbrocoma cinerea\u003c/em\u003e is included for comparison. Bold values on the diagonal represent intraspecific distances.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\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\u003cem\u003eA. boliviensis\u003c/em\u003e (clade A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003cem\u003eA. boliviensis\u003c/em\u003e (clade B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003cem\u003eA. boliviensis\u003c/em\u003e (clade C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003cem\u003eA. cinerea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.1\u003c/b\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\u003eValues of nucleotide diversity and polymorphic sites are highly dependent on whether samples of \u003cem\u003eA. boliviensis\u003c/em\u003e are grouped or not (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When all samples (regardless of clade) are pooled, nucleotide diversity is 0.045 and 121 polymorphic sites are present. For comparison, samples of \u003cem\u003eA. cinerea\u003c/em\u003e (that span from southern Peru to northern Argentina) have a nucleotide diversity of 0.029 and 33 polymorphic sites. Once samples of boliviensis are grouped into clades, it becomes clear that the sample from clade C is inflating the values found for the whole species. When either clade A and B are considered separately, nucleotide diversity is reduced to 0.012 and 0.003, respectively while polymorphic sites are 28 and 4. And when clades A and B are combined, nucleotide diversity is 0.033 (slightly higher than in \u003cem\u003eA. cinerea\u003c/em\u003e) while polymorphic sites are more than double with 68 versus 33.\u003c/p\u003e \u003cp\u003eGiven that all samples of \u003cem\u003eA. boliviensis\u003c/em\u003e come from separate localities, every individual (except for the two samples from Pusuq'huni) has a unique haplotype in both clades (A and B) which is reflected in their high haplotype diversity. \u003cem\u003eFs\u003c/em\u003e values were positive in all cases for \u003cem\u003eA. boliviensis\u003c/em\u003e and only significant when clades A and B and B and C were combined. This value was negative and non-significant for \u003cem\u003eA. cinerea\u003c/em\u003e. A similar trend was found for Tajima\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eGenetic diversity of \u003cem\u003eA. boliviensis\u003c/em\u003e and \u003cem\u003eA. cinerea\u003c/em\u003e based on cytochrome b sequences. Values for each clade and different groupings are also included. n: number of individuals included, \u003cem\u003eh\u003c/em\u003e: number of haplotypes, \u003cem\u003eHd\u003c/em\u003e: haplotype diversity, \u003cem\u003eFs\u003c/em\u003e: Fu\u0026rsquo;s \u003cem\u003eFs\u003c/em\u003e statistic, and \u003cem\u003eD\u003c/em\u003e: Tajima\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e statistic. * indicates statistically significant results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNucleotide diversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolymorphic sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHd\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eFs\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. cinerea\u003c/em\u003e (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. boliviensis\u003c/em\u003e (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. boliviensis\u003c/em\u003e (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclade A\u0026thinsp;+\u0026thinsp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. boliviensis\u003c/em\u003e (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclade A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. boliviensis\u003c/em\u003e (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclade B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. boliviensis\u003c/em\u003e (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclade B\u0026thinsp;+\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.6*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eReduced-representation genome-level variation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter demultiplexing and size selection (130bp) the number of retained reads was 253 725 812. After running \u003cem\u003edenovo_map\u003c/em\u003e the number of loci and polymorphic loci shared by at least 80% of the individuals within \u003cem\u003eA. boliviensis\u003c/em\u003e was 1 322 883 and 1 002 230, respectively. For \u003cem\u003eA. cinerea\u003c/em\u003e it was 852 366 and 611 891. In addition, the number of variants retained after applying all filters in VCFtools was 2192 for \u003cem\u003eA. boliviensis\u003c/em\u003e and 2850 for \u003cem\u003eA. cinerea\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe PCA plot built from GBS data supports the groups formed in the \u003cem\u003eCyt-b\u003c/em\u003e tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), with the PC1 explaining 51.9% of the variance between the specimen from clade C (Tarija) and the rest of the samples and the PC2 explaining 20.4% of the variance found in the specimen from clade B (Santa Cruz). Because their distance in multivariate space is so marked, we could not assess any structure within clade A unless the other two clades were removed. By doing so, it became apparent that, as described by the \u003cem\u003eCyt-b\u003c/em\u003e tree, the sample from Sailapata is distanced from the rest and it explains 28.8% of the variation. The remaining samples show virtually no separation on the horizontal axis, but there is differentiation seen on the y-axis that explains 20.6% of the variation. The distribution of these samples seems to be determined by distance in a somewhat east to west fashion, from the eastern most sample in Sach\u0026rsquo;a Loma going towards the other side of the Rocha-Caine-Grande River system to the western samples from Pusuq\u0026rsquo;huni and Torotoro.\u003c/p\u003e \u003cp\u003eTo serve as a reference, the five samples of \u003cem\u003eA. cinerea\u003c/em\u003e that come from southern Peru, Bolivia, and northern Argentina do not display prominent structure other than the samples from northern Argentina and southern Bolivia (from the Reserva Nacional de Fauna Andina Eduardo Avaroa [RNFA Eduardo Avaroa]) grouping together, somewhat distanced from the other two (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Here, the PC1 explains 39.1% of the variation and the PC2 explains 29.3%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing the Evanno method, population structure in \u003cem\u003eA. boliviensis\u003c/em\u003e is better explained at K\u0026thinsp;=\u0026thinsp;2 when independent allele frequencies are used (Mean LnP = -23,790, SD\u0026thinsp;=\u0026thinsp;5.11, and ΔK\u0026thinsp;=\u0026thinsp;753.28). In this case, one population consists of all samples from clade A (with inferred ancestry of 1) and another population represents the sample from clade C (with inferred ancestry of 1). The sample from clade B shows admixture with most ancestry shared with clade A (0.658) and less with clade C (0.342) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Under a correlated allele frequency model, K\u0026thinsp;=\u0026thinsp;3 is selected as the best partition (Mean LnP = -18,837, SD\u0026thinsp;=\u0026thinsp;601.5, and ΔK\u0026thinsp;=\u0026thinsp;8.76) with a third cluster being assigned to samples CBF5455 (ancestry of 0.382) and TK161718 (ancestry of 0.572). Given the larger values of LnP and SD and the much smaller ΔK, this assignment of K\u0026thinsp;=\u0026thinsp;3 could be an artifact of the model which has a risk of over-estimating K. Finally, analyses for \u003cem\u003eA. cinerea\u003c/em\u003e were not able to detect any population structure within the samples, which is most likely due to the low number of samples included (n\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e \u003cp\u003eThe EEMS plot of \u003cem\u003eA. boliviensis\u003c/em\u003e shows two areas of lower-than-average gene flow, one between samples from clade A and B in southern Cochabamba and the other one surrounding the sample from clade C in Tarija. Both areas have low intensity (lightest shade of orange). One area of higher-than-average gene flow is portrayed in the mesothermic valleys of central Cochabamba where most of clade A comes from. In the case of \u003cem\u003eA. cinerea\u003c/em\u003e, the low number of samples and their dispersal on the landscape generated low intensity areas of higher-than and lower-than-average gene flow that are located where the samples are (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTests of isolation by distance suggest that the structure seen in \u003cem\u003eA. boliviensis\u003c/em\u003e might be explained by other biological scenarios. The histograms in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that the original value of the correlation between distance matrices (the black dot) falls inside the reference distribution (the histogram itself) indicating that isolation by distance is not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAnalyses of both cytochrome b and genomic reduced representation make it clear that samples of \u003cem\u003eAbrocoma boliviensis\u003c/em\u003e evaluated in this study represent three separate clades, as evidenced by the high values of genetic distance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), little to no admixture (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and long divergence times between lineages (Arenas-Viveros et al. In prep). In addition, the highly fragmented and transformed landscape, which might be imposing higher isolation, places each of these clades and the species as a whole at a higher risk of extinction.\u003c/p\u003e \u003cp\u003eThe genetic structuring found within A. boliviensis, namely clades A, B and C, is evident in both the Cyt-b gene tree and the PCA analyses performed with GBS data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This same structure was supported, to an extent, by the STRUCTURE analysis where clades A and C form their own cluster with no shared ancestry between them, and clade B (or sample MNK3851) shares ancestry with both clades, with a higher percentage being shared with members of clade A. In addition, we found that these genetic clusters are geographically and ecologically structured. Clades A and C occur in different ecoregions (Yungas or Dry Interandean dominated region vs. mosaic of Boliviano-Tucumano and Dry Interandean forests) and divergence time estimates between the three clades are in the order of millions of years (Arenas-Viveros et al. In prep), suggesting that these lineages have been on separate evolutionary pathways and should therefore be identified, at minimum, as separate ESUs (Evolutionary Significant Units).\u003c/p\u003e \u003cp\u003eA case could be made to elevate each clade to the hierarchy of subspecies or alternatively, to elevate clades A and B to subspecies level and assign the individual from southern Bolivia (i.e., Clade C) to a possible new species, considering the high levels of genetic distance between clades (7\u0026ndash;13%), habitat differences, and divergence times. However, we prefer to take a much more reserved position in the understanding that genetic divergences by themselves are not taxonomic characters. Moreover, comparative studies on the morphology and ecology of members of each clade are lacking, and until that piece of the puzzle is added, it would be difficult to draw well-supported conclusions on the taxonomy of the species. For instance, another Andean highland specialist, the vicuna (\u003cem\u003eVicugna vicugna\u003c/em\u003e), is divided into subspecies from the Wet and Dry Puna respectively, and this taxonomic arrangement is supported by morphological and genetic traits (Mar\u0026iacute;n et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Not to mention that it is necessary to add more data from clades B and C and confirm whether the lack of samples from the department of Chuquisaca (i.e., the area between clades B and C along the eastern Andes) is biologically significant or more of a sampling artifact. For example, the distribution of the Andean cat \u003cem\u003eLeopardus jacobita\u003c/em\u003e, is considered naturally fragmented due to its preference for high altitude environments and the fact that no individuals have been recorded between 30 and 35\u0026deg;S (Coss\u0026iacute;os et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Regardless, the potential for a taxonomic rearrangement is there, and in fact similar studies on rare and/or endangered species have revealed cryptic diversity that support the assignment of new species or confirmed the existence of subspecies (Castillo-Morales et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Degner et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Murphy et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLevels of nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e) and number of polymorphic sites in the Bolivian chinchilla rat are undoubtedly affected by whether ESUs are grouped or evaluated individually. Just including the specimen from Tarija (i.e., clade C) doubles the number of polymorphic sites (121 vs 68) and adds 36% more nucleotide diversity (0.045 vs 0.033) than when not included (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to values of its sister clade \u003cem\u003eAbrocoma cinerea\u003c/em\u003e (\u003cem\u003eπ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029 and 33 polymorphic sites), nucleotide diversity is similar only when Clades A\u0026amp;B are combined, but the number of polymorphic sites is still double for \u003cem\u003eA. boliviensis\u003c/em\u003e. This is interesting because the samples of \u003cem\u003eA. cinerea\u003c/em\u003e used in this study cover a large geographic area through the Altiplano, from southern Peru to northern Argentina (~\u0026thinsp;1,015 km linear distance). Meanwhile, the northern most sample of \u003cem\u003eA. boliviensis\u003c/em\u003e (from Sailapata in Cochabamba) is about 527 km (linear distance) from the sample collected in Tarija and only\u0026thinsp;~\u0026thinsp;340 km from Clade B in Santa Cruz.\u003c/p\u003e \u003cp\u003eThis fact highlights how more genetic diversity and polymorphisms are contained in a species whose distribution is restricted to one slope of the Andes and one country, than in one spread along a much larger area in the continent. In contrast, when values were calculated for clades A and B separately, nucleotide diversity drops significantly (\u003cem\u003eπ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012 and 0.003 respectively) while polymorphic sites reach levels like those of \u003cem\u003eA. cinerea\u003c/em\u003e for Clade A (28) and are remarkably fewer than in clade B (4). This comparison indicates how each ESU carries unique diversity that is not found in other populations, and reinforces the hypothesis that any impact on one of these lineages might result in drastic genetic loss for the species; furthering their case for separate efforts in their monitoring, research, and conservation (Borges et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eValues of Fu\u0026rsquo;s \u003cem\u003eFs\u003c/em\u003e and Tajima\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e are all positive for \u003cem\u003eA. boliviensis\u003c/em\u003e regardless of grouping arrangement and only significant for \u003cem\u003eFs\u003c/em\u003e when clades A\u0026thinsp;+\u0026thinsp;B and B\u0026thinsp;+\u0026thinsp;C are evaluated, suggesting a scenario of population bottleneck or the effect of balancing selection. Values for \u003cem\u003eA. cinerea\u003c/em\u003e on the contrary, are negative but non-significant for both estimates, indicating possible scenarios of population expansion. Similar results were obtained by Castillo-Morales et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), in which two newly discovered lineages of the endangered leatherback turtle (\u003cem\u003eDermochelys coriacea\u003c/em\u003e) had positive values for both \u003cem\u003eFs\u003c/em\u003e and \u003cem\u003eD\u003c/em\u003e, while samples of the less threatened olive ridley turtles (\u003cem\u003eLepidochelys olivacea\u003c/em\u003e) had negative values for the same estimators. In their study, these results coincided with what was already known about these species: populations of leatherback turtles are declining while those of \u003cem\u003eL. olivacea\u003c/em\u003e have been increasing in the last decades. Unfortunately, there are no long-term estimates of population trends in any species of abrocomid, but the results of our study inform and provide an approximation to the status of these populations. Taken at face value, our results suggest that populations of \u003cem\u003eAbrocoma boliviensis\u003c/em\u003e may have gone through genetic bottlenecks which, in combination with the small distribution range of the species, warrants further conservation investments.\u003c/p\u003e \u003cp\u003eThe evolutionary history of extant abrocomids traces its origins to about 10Ma, when the Andes and its surrounding lowlands where still experiencing orogenic changes that in turn transformed the landscape and promoted divergence (Garzione et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Graham \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gregory-Wodzicki \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The ancestor of A. boliviensis was on its own evolutionary pathway shortly before 6 Ma when the eastern cordillera experienced a second uplift (Arenas-Viveros et al. In prep). At this point, the eastern slope of the Andes has had its characteristic corrugated topology since its first uplifted about 10 Ma, but as the mountain chain kept elevating, this jagged landscape created barriers for the movement of taxa at mid and now also, high elevations (Graham \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moreover, palaeobotanical data provides evidence for the expansion of the flora typical of the northern region (i.e., Yungas forests) along with the uplift (Graham et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), while Seasonally Dry Tropical Forests which had a continuous distribution from the Andean foothills to the Caatingas in Brazil during the upper Pleistocene, became disjunct and fragmented by other habitat types (e.g., Tucumano-Boliviano forests in the southern distribution of A. boliviensis) as a result of dry-cold vs humid-warm climatic cycles (Mogni et al. 2015). These historical barriers to migration along with adaptations to different environments (i.e., the different ecoregions inhabited by northern and southern samples) and modern-day transformation of the landscape, could explain the levels of divergence and the little to no of admixture found between lineages.\u003c/p\u003e \u003cp\u003eAs mentioned before, the area where the Bolivian chinchilla rat is distributed is one of the most transformed and exploited by humans in the country (Brandt and Townsend \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Killeen et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This creates another source of pressure for the species, because even if some of its populations are doing well, this might not be the case as the environment changes in the future. Compared to the Andean cat, another species of endangered mammal (Coss\u0026iacute;os et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the Degu \u003cem\u003eOctodontomys gliroides\u003c/em\u003e, another rodent from the Altiplano (Rivera et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), haplotype and nucleotide diversity are both higher in \u003cem\u003eA. boliviensis\u003c/em\u003e regardless of ESU grouping. Nevertheless, these ESUs have been isolated from one another by past processes and continue to be by modern transformation and degradation of the landscape. One of the main purposes of identifying and conserving ESUs is to maintain genetic diversity that will maximize the evolutionary and adaptive potential of a species in the face of environmental change (Funk et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This is of special importance in the eastern Andes because in addition to human-driven transformation, studies have shown that vegetation shifts in the past (mostly of \u003cem\u003ePolylepis\u003c/em\u003e woodlands within the Andean grasslands) have been mediated by variations in temperature, precipitation and burning regime (Williams et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), all of which are incremented by climate change. \u003cem\u003eAbrocoma boliviensis\u003c/em\u003e is known to be associated with forested areas, and unlike other abrocomids in the Altiplano that inhabit more open environments (Patton and Emmons \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), these changes to the landscape threaten the future of its populations unless intervention for the management and conservation of the native landscape takes place.\u003c/p\u003e \u003cp\u003eIn addition to being endangered and endemic, the Bolivian chinchilla rat is also rare, but this might present an advantage in at least one aspect of its conservation. When evaluating areas to potentially designate as protected, using one taxonomic group as a proxy to community diversity might not be the best approach because of the low congruence between geographic patterns of diversity across taxonomic groups (e.g., vertebrate classes or terrestrial vs. aquatic organisms). On the other hand, while rare species are the most likely to be lost if they are not protected, they are one of the best indicators when selecting sites with the aim of preserving diversity (Lawler et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In fact, protected areas that have been delineated with the purpose of conserving rare species have been shown to protect biodiversity more generally (Drever et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, the presence and maintenance of rare species contributes to, and supports, the functional structure of assemblages and ecosystems (Leit\u0026atilde;o et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mouillot et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). All of this, along with the potential to assign an economic value to the conservation of \u003cem\u003eA. boliviensis\u003c/em\u003e (e.g., from ecotourism and conservation donations for a charismatic species), might serve to persuade the appropriate authorities to provide the support needed to launch systematic monitoring and research programs (Drever et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLastly, based on the results presented herein and considering how much is still not known of \u003cem\u003eA. boliviensis\u003c/em\u003e, this study highlights the urgency with which survey efforts must become the first order of action. Once more population-level data becomes available, demographic, ecological, and genetic studies should provide a better understanding of the species and the evolutionary trajectory of its lineages, including whether a taxonomic rearrangement is needed. This in turn, will better inform any management and conservation actions, including areas for monitoring, creation of protected areas and corridors, as well as mitigation plans to reduce the effects of anthropogenic transformation of the landscape.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eAuthor DAV has received research support from The Graduate School and Biological Sciences Department, including the J Knox Jone and the Fonseca Mammal Scholarships, at Texas Tech University. JSB acknowledges funding from The Mohamed Bin Zayed Species Conservation Fund, The Texas Tech University Scholarship Catalyst Program and the Fulbright US Scholars Program.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eD.A.V and J.S.B concieved and designed the study, D.A.V analyzed data, interpreted results, and wrote the main manuscript text, T.T. and X.V.L provided critical revisions. All authors contributed materials and reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe following individuals and institutions were instrumental in providing loan material or facilitating access to their collections, without which this study would not have been possible: Julieta Vargas (formerly at Colecci\u0026oacute;n Boliviana de Fauna, Museo Nacional de Historia Natural de Bolivia), Freddy Navarro, Luis Aguirre, Renzo Vargas (Centro de Biodiversidad y Gen\u0026eacute;tica, Cochabamba, Bolivia), Kathia Rivero, Luis Acosta (Colecci\u0026oacute;n de Mam\u0026iacute;feros, Museo de Historia Natural \u0026quot;Noel Kempff Mercado\u0026quot;, Santa Cruz, Bolivia), Ricardo C\u0026eacute;spedes (Museo de Historia Natural \u0026ldquo;Alcide d\u0026apos;Orbigny\u0026rdquo;, Cochabamba, Bolivia), Robert Bradley, Heath Garner (Natural Science Research Laboratory, Texas Tech University), Jim Patton, Chris Conroy (Museum of Vertebrate Zoology, University of California), Michael Mares, Janet Braun (Sam Noble Museum of Natural History, The University of Oklahoma), Evaristo Lopez, Cesar Medina (Museo de la Universidad Nacional San Agust\u0026iacute;n de Arequipa, Per\u0026uacute;). Further, we would like to thank the following colleagues for their efforts in securing specimens in the field: Carola Azurduy, Nuria Bernal Hoverud, Jasper K. Rasmussen, Oliver Quinteros, and Fray Andr\u0026eacute;s Langer (deceased). We thank the Andean Carnivore Conservation Program for their efforts in obtaining a unique sample at the southern edge of the known distribution of A. boliviensis.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files. Sequence data will be deposited in GenBank.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBernal N (2016) Abrocoma boliviensis. The IUCN Red List of Threatened Species 2016. https://dx.doi.org/10.2305/IUCN.UK.2016-2.RLTS.T18A22182349.en.\u003c/li\u003e\n\u003cli\u003eBorges VS, Santiago PC, Lima NGS, Coutinho ME, Eterovick PC, Carvalho DC (2018) Evolutionary Significant Units within Populations of Neotropical Broad-Snouted Caimans (Caiman latirostris, Daudin, 1802).\u003cem\u003e \u003c/em\u003eJournal of herpetology 52 (3):282-8. doi: 10.1670/17-074\u003c/li\u003e\n\u003cli\u003eBrandt JS, Townsend PA (2006) Land use - land cover conversion, regeneration and degradation in the high elevation Bolivian Andes.\u003cem\u003e \u003c/em\u003eLandscape ecology 21 (4):607-23. doi: 10.1007/s10980-005-4120-z\u003c/li\u003e\n\u003cli\u003eCastillo-Morales CA, S\u0026aacute;enz-Arroyo A, Castellanos-Morales G, Ru\u0026iacute;z-Montoya L (2023) Mitochondrial DNA and local ecological knowledge reveal two lineages of leatherback turtle on the beaches of Oaxaca, Mexico.\u003cem\u003e \u003c/em\u003eScientific reports 13 (1):8836-. doi: 10.1038/s41598-023-33931-4\u003c/li\u003e\n\u003cli\u003eCoss\u0026iacute;os ED, Walker RS, Lucherini M, Ruiz-Garc\u0026iacute;a M, Angers B (2012) Population structure and conservation of a high-altitude specialist, the Andean cat \u003cem\u003eLeopardus jacobita\u003c/em\u003e.\u003cem\u003e \u003c/em\u003eENDANGERED SPECIES RESEARCH 16:283-94.\u003c/li\u003e\n\u003cli\u003eDanecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, et al. 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The Bolivian chinchilla rat, \u003cem\u003eAbrocoma boliviensis\u003c/em\u003e, is currently threatened by its rarity, paucity of information about its natural history, and landscape transformation driven by anthropogenic activities. Given the conservation status and limited distribution of \u003cem\u003eA. boliviensis\u003c/em\u003e, understanding how its genetic diversity is apportioned is crucial to inform any potential conservation efforts. In this study, we assessed the genetic diversity and population structure of \u003cem\u003eA. boliviensis\u003c/em\u003e as a first approximation to a comprehensive evaluation of the species. Mitochondrial data from 11 individuals of \u003cem\u003eA. boliviensis\u003c/em\u003e reveal high levels of genetic distance, nucleotide diversity and polymorphisms, all of which indicate the existence of three separate clades. This is further supported by reduced representation genomic data that shows little to no admixture between these clades, suggesting that these lineages have been on separate evolutionary pathways and should be identified, at minimum, as separate evolutionary significant units. Our contribution highlights the urgency with which survey efforts must become the first order of action, and how new population-level data will provide a better understanding of the species, the evolutionary trajectory of its lineages, and the steps to take towards its conservation.\u003c/p\u003e","manuscriptTitle":"Population genetics and lineage structure of the endangered Bolivian chinchilla rat Abrocoma boliviensis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 09:41:19","doi":"10.21203/rs.3.rs-6513653/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-01T19:18:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T23:05:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203791556435801843179751086548122952962","date":"2025-10-23T22:54:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T20:13:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172404458448367573023371840593448162599","date":"2025-05-29T14:25:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259081935395792846499738465445115920133","date":"2025-05-12T22:21:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T18:58:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-26T01:55:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T01:52:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2025-04-23T14:31:22+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":"a624715b-ab66-4422-b81b-60544cdcfb26","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T15:18:07+00:00","versionOfRecord":{"articleIdentity":"rs-6513653","link":"https://doi.org/10.1007/s10592-026-01789-4","journal":{"identity":"conservation-genetics","isVorOnly":false,"title":"Conservation Genetics"},"publishedOn":"2026-05-02 15:57:02","publishedOnDateReadable":"May 2nd, 2026"},"versionCreatedAt":"2025-05-05 09:41:19","video":"","vorDoi":"10.1007/s10592-026-01789-4","vorDoiUrl":"https://doi.org/10.1007/s10592-026-01789-4","workflowStages":[]},"version":"v1","identity":"rs-6513653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6513653","identity":"rs-6513653","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}