ORIGINAL ARTICLE Genetic Resources (2022), 3 (6), 1–14 DOI: 10.46265/genresj.UYML5006 https://www.genresj.org ISSN: 2708-3764 Genetic differentiation between coexisting wild and domestic reindeer (Rangifer tarandus L. 1758) in Northern Eurasia Gulnara R Svishcheva a,b, Olga V Babayan c, Taras P Sipkod, Sergey N Kashtanov a, Marina V Kholodovad and Yurii A Stolpovsky a a Vavilov Institute of General Genetics, Russian Academy of Sciences, 119333, Moscow, Russia b Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090, Novosibirsk, Russia c Skolkovo Innovation Centre, Gordiz Ltd, 121205, Moscow, Russia d A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, 1190713, Moscow, Russia Abstract: Rangifer tarandus L. 1758 is one of the few modern hoofed species in which domestic and wild forms coexist in the same territory. The genetic differentiation of domestic and wild reindeer in Northern Eurasia was examined using microsatellite data. A total of 780 animals were studied at 16 microsatellite loci. Samples of wild reindeer were taken from seven populations inhabiting different natural areas, and samples of domestic animals were selected from the Evenki, Evens, Chukchi and Nenets breeds, including two ecotypes, Tofalar and Todzha reindeer. The levels of genetic diversity and variation in wild reindeer were higher than in domestic ones. Bayesian clustering analysis allowed us to distinguish domesticated reindeer populations by the degree of taming, but failed to detect differences in genetic structure between wild reindeer populations. These differences were found using the pairwise Fst values. Overall, the microsatellite analysis revealed a significant genetic differentiation between domestic and wild forms and the structuring of populations within each form, which may be important for the development of strategies for animal conservation. Keywords: Domestic and wild forms of reindeer, geographic population, microsatellites, phylogenetic tree, biodiversity Citation: Svishcheva, G. R., Babayan, O. V., Sipko, T. P., Kashtanov, S. N., Kholodova, M. V., Stolpovsky, Y. A. (2022). Genetic differentiation between coexisting wild and domestic reindeer (Rangifer tarandus L. 1758) in Northern Eurasia. Genetic Resources 3 (6), 1–14. doi: 10.46265/genresj.UYML5006. © Copyright 2022 the Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Introduction Reindeer (Rangifer tarandus L. 1758) is a widespread circumpolar species on the planet. The geographic distribution of reindeer mainly covers the northern part of the Arctic region, including arctic and subarctic regions of Eurasia and North America (Williams and Heard, 1986). Although the range of reindeer is very large, the species has been classified as vulnerable by the International Union for Conservation of Nature (IUCN) because of a 40% decline over the last decades (Gunn, 2016). The decline in population size is the result of the intensification of industrial development in the Arctic and climate warming (Yannic et al, 2014). For the indigenous Arctic ethnic groups living in Northern Eurasia, reindeer are of great economic, social, cultural and ecological importance, since they are a source of meat, hide and milk, as well as a means for transportation. In the historical process, the employment of reindeer has been of crucial importance in the colonization of the northernmost parts of Eurasia. Currently, reindeer continue to play a central role in the cultures of the Indigenous Nenets, Chukchi, Evenki, Sami, Evens Peoples and other Peoples of Northern Eurasia (Helskog and Indrelid, 2011; Bjørklund, 2013). Reindeer husbandry is undoubtedly a historical branch of animal keeping in the northern regions of Rus- sia. Unlike other Arctic countries, reindeer husbandry in Russia is much differentiated. Representatives of 18 peo- ples are engaged in the industry, thereby preserving their national traditions, and 16 of them are included in the Received: 01.12.2021 Accepted: 15.04.2022 Published online: 08.07.2022 https://www.genresj.org https://www.dx.doi.org/10.46265/genresj.UYML5006 https://www.genresj.org https://www.dx.doi.org/10.46265/genresj.UYML5006 2 Svishcheva et al Genetic Resources (2022), 3 (6), 1–14 official list of Indigenous Small-Numbered People of the North (Jernsletten and Klokov, 2002). By now, four native (Nenets, Evens, Evenki and Chukchi) breeds of reindeer have been officially registered in Russia. The Nenets breed is widespread in the Taymyr District of the Krasnoyarsk Territory, as well as in the Yamal-Nenets, Nenets and Khanty- Mansi Autonomous Districts, the Komi Republic and the Murmansk Region. Currently, this reindeer breed is the most numerous (over 880,000 heads) and prosperous. The Chukchi breed inhabits the territory of the Chukotka and Kamchatka peninsulas and the northeast of Yakutia. The Evenki breed is bred in the taiga zone of Siberia and the Far East, from the Yenisei River region to the shores of the Sea of Okhotsk and Sakhalin Island. The Evens breed habitat is the mountain-taiga regions of Yakutia and Kamchatka, as well as the region of Magadan (by its type, this breed occupies an intermediate position between the Chukchi and Evenki breeds). All breeds differ in productivity and conformation, as well as in adaptation to specific natural and climatic conditions. Based on our own observations and FAO data (Zabrodin and Borozdin, 1989), we summarized the descriptive characteristics of the four breeds mentioned above (Table 1 ). The breed-forming processes in reindeer husbandry have much in common with the breed genesis of other species of domesticated aboriginal animals, such as horses, sheep, yaks, camels, etc. The reindeer breed specialty is based on the folk traditional breeding system and the complete lack of commercial incentives, features that later also appear in stud breeding in Europe and North America (Kharzinova et al, 2015). All the reindeer breeds are aboriginal and the result of folk selection by various northern people (Zabrodin and Borozdin, 1989; Kharzinova et al, 2015). Within the breeds, there are some ecotypes that are characterized by their own morphological features, but they are taxonomically indistinguishable (Davydov et al, 2007). In particular, within the Evenki breed, two independent ecotypes, Tofalar and Todzha reindeer, have been identified (Kharzinova et al, 2015). The Tofalar reindeer, bred by the small indigenous ethnic group, Tofalars, are the largest of the domesticated reindeer. Currently, Tofalar reindeer husbandry numbers less than 100 heads, compared with several thousand in the 1990s, and the population of the Todzha reindeer, bred by Todzha-Tuvans, is less than 1,000 animals. The Tofalar and Todzha herds, which define the southern borders of reindeer husbandry, are the most isolated from other reindeer herds bred by the Indigenous Peoples. The reindeer husbandry of the Tofalars and that of the Todzha-Tuvans can be combined into one Sayan group of reindeer husbandry, based on their common characteristics. In Russia, along with domesticated reindeer, there are many wild herds, whose ranges cover almost the entire tundra, forest-tundra and taiga zones, including mountainous areas. Depending on the habitat of wild reindeer, several ecological forms (macro- ecotypes) are distinguished, namely island (inhabiting arctic deserts), tundra (migrating to the forest-tundra for the winter), taiga and mountain-taiga (carrying out vertical migrations) (Davydov et al, 2007). The most significant differences in size are observed between the tundra and taiga forms of reindeer. As a rule, forest reindeer are more long-legged animals, with elongated body. However, the intraspecific taxonomy of Rangifer tarandus L. is still controversial and needs to be specified using molecular genetic markers. On the territory of Eurasia, several subspecies of wild reindeer are distinguished (Danilkin, 1999). The Svalbard reindeer (R. tarandus platyrhynchus) is a non-migratory tundra subspecies inhabiting the high- arctic archipelago of Svalbard. The Novaya Zemlya reindeer (R. tarandus pearsoni) is an island subspecies living only on the northern island of the Novaya Zemlya archipelago. The European reindeer (R. tarandus tarandus) is a subspecies that lives in the European part of Eurasia in the tundra and northern taiga regions up to the Ural Mountains. The Siberian reindeer (R. tarandus sibiricus) is a subspecies that lives in tundra and partly forest-tundra zones of Siberia (from the Ural Mountains to the Lena River and Lake Baikal). Finally, the Okhotsk reindeer (R. tarandus phylarchus) is a subspecies inhabiting the coast of the Sea of Okhotsk, the Amur River basin, the northern part of Sikhote-Alin, the Kamchatka peninsula and Sakhalin Island (from the Lena River and Lake Baikal to the Sea of Okhotsk). These geographic subspecies differ in the body type, increasing in size with the transition of the habitat from west to east, but genetic and phenotypic variabilities and differences in subspecies have been insufficiently studied. It is worth noting that the bulk of the wild reindeer populations (~85%) is concentrated in the Taymyr Peninsula, northern Yakutia, and the central part of Chukotka (Kolpaschikov et al, 2015; Kharzinova et al, 2018). The Taymyr herd of wild reindeer is the largest and most monitored wild reindeer population in Eurasia, inhabiting a vast area in the north of central Siberia (Petrov et al, 2012; Kolpaschikov et al, 2015). Three large, relatively isolated herds of wild reindeer are found in the continental tundra of Yakutia, namely Yana-Indigirka, Sundrun (Indigirka- Kolyma) and Lena-Olenek (Bulun) (Safronov, 1996). In addition, there are sedentary taiga reindeer populations, distributed primarily in the mountains of the south and west Yakutia. In recent years, a difficult demographic situation has developed with the populations of wild reindeer on the Kola Peninsula (Baranova et al, 2016). The main direct cause of the decline in these populations is poaching, while forest fires and deforestation, leading to the depletion of food resources, are considered indirect causes. Wild reindeer of the Murmansk region of the Russian Federation (the Kola Peninsula) are endangered and listed in the latest Red Data Book of Murmansk Region (Konstantinova et al, 2014). On the contrary, the number of wild reindeer in the Magadan Genetic Resources (2022), 3 (6), 1–14 Genetic differentiation of reindeer populations 3 region is growing thanks to constant monitoring and restrictions on poaching. Wild and domesticated reindeer have distinct cran- iological parameters and differ genetically according to results obtained by various methods of biochemi- cal genetics (Kharzinova et al, 2017). DNA markers are the most popular tool for studying genetic diver- sity of reindeer populations. The analysis of mitochon- drial DNA polymorphism is widely used in phyloge- netic studies (Flagstad and Røed, 2003; Davydov et al, 2007; Wang et al, 2019; Røed et al, 2020). Microsatel- lite markers are widespread for studying the genetic structure and allelic pools of individuals, as well as for their identification a nd d ifferentiation ( Røed and Midthjell, 1998; Jepsen et al, 2002; Mcloughlin et al, 2004; Mcdevitt et al, 2009; Kharzinova et al, 2016; Zhai et al, 2017; Stolpovsky et al, 2020). With the devel- opment of high-throughput SNP genotyping platforms, SNP markers are getting more popular in animal agricul- ture. Although SNP platforms are becoming available for genomic research in reindeer, they are not cost-effective for genotyping several hundred animals. This study aimed to identify genetic diversity among poorly examined wild reindeer populations and genetic differences between wild and coexisting domesticated reindeer living in different eco-climatic zones using microsatellite data. Materials and methods Animal sample information Initially, 790 animals were collected, which were selected from all breeds registered in Russia (Nenets, Evens, Evenki and Chukchi), including two ecotypes (Tofalar and Todzha reindeer), as well as from seven wild populations of Magadan, Amur, Taymyr, Yakutia, Tura, Murmansk and Chukotka regions (Figure 1 and Supplemental Table S1). For the study of domesticated reindeer, we selected clinically healthy animals accord- ing to phenotypic traits corresponding to their breeds. The age of the selected animals varied from 3 to 7 years. Special attention was paid to calving females, since only females with a good conformation, regularly bearing healthy calves and capable of raising strong offspring, should be selected for further reproduction. For females, the presence of a calf was determined by the condi- tion of the udder and by interviewing reindeer herders. Males were selected in August-September, and females were selected in October-November, i.e. during periods of their maximum fatness. For the study of wild reindeer, we used the biological material from animals that were obtained from hunters. As biological material, pieces of the cartilaginous part of the horn or auricle were taken, which were placed in test tubes and fixed with ethanol (96%). A total of 13 population samples were tested. It is worth noting that the sample of Nenets reindeer breed combined three subsamples taken from different breed- ing regions, since no significant genetic differences have been found between these subsamples (Stolpovsky et al, 2020). DNA extraction and microsatellite genotyping Animals were genotyped using 16 polymorphic microsatellite markers (BMS1788, BMS745, C143, C217, C276, C32, FCB193, NVHRT16, OHEQ, RT1, RT24, RT30, RT6, RT7, RT9 and T40). The description of microsatellites is given in Supplemental Table S2. DNA was extracted from velvet antlers, muscles, skin, and ear notches using the COrDIS SPRINT PCR com- patible reagent kit (OOO Gordiz Moscow, Russia, https://gordiz.ru/en/products/animal-kits/cordis-rangifer/) according to the protocol of the manufacturer. PCR was performed using Applied Biosystems thermal cyclers under the conditions recommended by the manufacturer of the used reagent kit. Separation of PCR products was carried out by capillary electrophoresis using an ABI 3130 automatic genetic analyzer (Applied Biosystems). Data analysis and determination of allele variants in the specimens for each locus were performed using the GeneMapper ID-X 1.4 software (Applied Biosystems). The genotyping quality was controlled using a standard specimen supplied as part of the reagent kit. For each animal, the genotypes of microsatellites were obtained as the allelic lengths in base pairs. The used multiplex short tandem repeat (STR) panel consisted of ten dinucleotide loci (Rt6, BMS1788, Rt30, Rt1, Rt9, Rt7, Rt24, FCB193, BMS745 and NVHRT16) and six tetranucleotide STR markers (OHEQ, C217, C32, 40, C276 and C143). Of these, ten loci have been described for caribou: NVHRT16, BMS745, FCB193, OHEQ, BMS1788, RT6, RT24, RT30, RT9, RT1 and RT7 (Yannic et al, 2014), and other six loci have been described for North American subspecies of red deer: C32, C143, C276, T40 and C217 (Jones et al, 2002) (Meredith et al, 2005) . This panel has recently been tested on reindeer (Stolpovsky et al, 2020; Dodokhov et al, 2021). Statistical analysis The minimum number of markers needed to discrim- inate between animals was estimated using the geno- type curve() function with 50,000 replicates from the poppr R package (Kamvar et al, 2014). In each popula- tion sample, the individuals with duplicated genotypes were removed using the clonecorrect() function from the same package. Linkage disequilibrium between loci was assessed using the pair.ia() function with 50,000 replicates from the same package. Testing the depar- ture from Hardy–Weinberg equilibrium (HWE) was per- formed using the hw.test function from the pegas R pack-age (Paradis, 2010). Here we applied two tests: the classical c2-test based on the expected genotype fre-quencies calculated from the allelic frequencies, and an exact test based on Monte Carlo permutations of alleles (with 50,000 replicates). To control the false discovery rate, the p-values of both HWE tests were https://gordiz.ru/en/products/animal-kits/cordis-rangifer/ 4 Svishcheva et al Genetic Resources (2022), 3 (6), 1–14 Table 1. Comparative characteristics of four domestic breeds of reindeer in Russia. Characteristics were summarized from our observations and FAO data (Zabrodin and Borozdin, 1989). Breed Predominant pelage colour Animal physique Live weight of males before rut (♂) and females (♀) [kg] Slaughter yield of live weight [%] Chukchi Dark brown Short height, squat, wide and at the same time elongated body, short limbs, well-developed skeleton and musculature. ♂: 125-130 ♀: 93-96 ~51-55 Nenets Brown and dark brown Medium height, rather long and wide body, well-developed skeleton, relatively wide head. ♂: 130-135 ♀: 90-95 ~50 Evenki Light brown and grey of various shades Tall height, rather long body, deep chest, well-developed skeleton and musculature ♂:140-175 ♀: 108-120 ~49 Evens Light brown and grey colour of various shades Tall height, long body, a narrow deep chest, narrow long pelvis, well-developed skeleton, narrow long head. ♂: 130-140 ♀:91-103 ~49-51 Figure 1. Geographical positions of the studied reindeer samples. The population samples are numbered as follows. Breeds: 1. Nenets; 2. Chukchi; 3. Evens; 4. Evenki; 5. Todzha; 6. Tofalar. Wild reindeer herds: 7. Taymyr; 8. Tura; 9. Murmansk; 10. Amur; 11. Magadan; 12. Yakutia; 13. Chukotka. The map was downloaded from https://www.google.com/maps. Genetic Resources (2022), 3 (6), 1–14 Genetic differentiation of reindeer populations 5 adjusted using the p.adjust R function with the option method=‘fdr’ (Benjamini and Hochberg, 1995). The main population genetic statistics for each reindeer sam- ple were computed in the adegenet R package (Jom- bart, 2008). Allele frequencies, allelic richness and the number of private alleles were assessed by the Pop- GenReport R package. Polymorphism information con- tent was estimated using the polysat R package (Clark and Jasieniuk, 2011). For each ‘locus-population’ pair, fixation index and the observed and expected het- erozygosity were estimated using the divBasic() func- tion from the the diveRsity R package (Keenan et al, 2013). Nei’s pairwise Fst and corresponding p-values were obtained by the pairwise.fst() function from the hierfstat R package (Goudet, 2005) and visualized by the corrplot R package (Wei et al, 2017). Fit, Fst and Fis for each locus were calculated using the Fst() func- tion from the pegas R package. The Bayesian clustering approach implemented in the STRUCTURE v.2.3.4 pro- gramme (Pritchard et al, 2000) was used to study pop- ulation differentiation. The optimal number of clusters was determined by the method proposed by (Evanno et al, 2005) and implemented in the Structure Har- vester and Clumpak programs (Earl, 2012; Kopelman et al, 2015). The dendrogram based on Nei’s genetic dis- tances was constructed by the neighbour-joining algo- rithm implemented in the poppr R package, with boot- strap support from 5,000 replicates. Isolation by dis- tance was assessed by regressing pairwise genetic dis- tance against the natural logarithm of geographical dis- tance (km) with the Mantel test (Mantel, 1967) with 10,000 permutations using the ade4 R package (Dray and Dufour, 2007). Slatkin’s linearized Fst was adopted as the measure of genetic distance (Rousset, 1997). The geographic centre of allelic richness among the stud- ied populations was calculated as the weighted mid- point from a series of latitude/longitude coordinates by a method that converts longitude/latitude coordinates (in radians) to a 3D Cartesian coordinate system (x, y, z): x = cos(lat) cos(lon), y = cos(lat) sin(lon) and z = sin(lat). This method then calculates the weighted means of these coordinates ( − x, − y, − z) and converts them back to longitude/latitude coordinates using the formu- las: lon = atan2 ( − y, − x ) and lat = atan2 ( − z, √ − x 2 + − y 2 ) , where atan2 is a function defined as the angle in the Euclidean plane. Results Data quality checks Initially, 790 individuals were genotyped at 16 loci. To ensure that we had enough information to accurately identify multilocus genotypes, we estimated the mini- mum number of loci needed to discriminate between animals to be 15 markers and determined the presence of clones. According to the results, four individuals geno- typed by less than 15 microsatellites and six individu- als with duplicated genotypes were removed from fur- ther analyses. Linkage disequilibrium between loci was assessed using the standardized index of association, rd (Agapow and Burt, 2001), which corrects for sam- ple size; rd between loci was not high and varied from -0.019 to 0.072. A significant deviation from HWE was detected by both HWE tests considered here only in the sample of wild Yakut reindeer at the C276 locus (Sup- plemental Tables S3–S5). Since deviation from HWE is most often associated with genotyping errors, we had to exclude C276 from the data for the wild Yakut sample. Thus, the final dataset included 780 individuals. Genetic variability For the 16 microsatellites analyzed, a total of 236 alleles were found, with 3 (C143 and C217) to 30 (OHEQ) alleles per marker. The mean number of alleles per marker across all samples was 14.8. The average percentage of the total number of alleles observed per marker varied from 40.94% (domestic Tofalar reindeer) to 81.8% (wild Yakut reindeer) (Table 2). Detailed results for each marker and reindeer population studied are available in Supplemental Table S6. In all studied samples of reindeer, we found alle- les with high frequency (AF) (AF > 0.7). In partic- ular, C217.215, T40.302, C32.330 and C143.180 had the highest frequencies. The polymorphism informa- tion content (PIC) values were calculated for each combination ‘sample-locus’ (Supplemental Table S7). The highest polymorphism levels were found for pairs ‘W.Yakut–OHEQ’ (PIC = 0.920), ‘W.Taym–BMS1788’ (PIC = 0.915) and ‘W.Yakut–BMS1788’ (PIC = 0.911). The mean PIC value over loci and samples appeared to be rather high, 0.71 ± 0.05. Out of 236 alleles in 780 reindeer genotyped, 16 alleles were private alleles with allelic counts ≥ 2 and AF > 0.008 (Table 3). The largest number of private alleles per marker (3) was detected for BMS1788 and OHEQ, and the largest numbers of private alleles adjusted for sample size were in the wild Chukchi herd and Todzha ecotype (24.4% and 14.4%, respectively of their total numbers). The presence of private alleles with frequencies above 0.01 in the wild herds suggests that each of these herds most likely has a unique genepool (Svishcheva et al, 2020). In total, private alleles were found in four wild reindeer herds and two domesticated ones (Table 3). In each sample of animals, we evaluated genetic variability in terms of the number of alleles (A), allelic richness (Ar), observed (Ho) and expected (He) heterozygosity, and fixation index (Fis) (Table 2 and Supplemental Table S6). Ar ranged from 3.29 (Tofalar herd) to 5.39 (wild Yakut herd) with a mean of 4.60 ± 0.17. Overall, we found a significant correlation between Ar and He (R2. 0.96, p-value = 2.92 × 10−9). The highest Ho value (0.77) corresponded to the wild Taymyr herd and the lowest (0.57) to the Tofalar herd. For each sample, we computed Fis as (He-Ho)/He. For Evens and Chukchi breeds the Fis values were equal to zero, while for other samples, we observed a slight 6 Svishcheva et al Genetic Resources (2022), 3 (6), 1–14 Table 2. Population parameters for the domesticated and wild reindeer samples studied. Size: population sample size; N: the average number of animals genotyped per marker; A: the number of alleles per sample; %: the percentage of total alleles observed across population samples per population sample per locus; Ar: the mean allelic richness across markers; Ho and He: observed and expected heterozygosity, respectively; Fis: fixation index; IC(se): mean (standard error) of inbreeding coefficients estimated for all animals. Herds of wild reindeer from Taimyr, Tura, Murmansk, Amur, Magadan, Yakutia and Chukotka are designated as ’W.Taym’, ’W.Tura’, ’W.Murm’, ’W.Amur’, ’W.Magad’, ’W.Yakut’ and ’W.Chuk’, respectively. Domestic form Size N A % Ar Ho He Fis IC (se) Nenets 224 223.88 132 60.94 4.31 0.64 0.66 3.03E-02 0.167(0.017) Todzha 42 42 89 46.66 3.77 0.64 0.62 -3.23E-02 0.171(0.043) Tofalar 47 46.94 77 40.94 3.31 0.57 0.56 -1.79E-02 0.182(0.039) Evens 44 43.56 130 60.98 4.78 0.73 0.73 0.00E+00 0.152(0.040) Evenki 50 50 113 52.96 4.27 0.67 0.68 1.47E-02 0.165(0.041) Chukchi 118 118 131 58.87 4.43 0.7 0.7 0.00E+00 0.156(0.023) Wild form W.Taym 21 21 142 65.88 5.34 0.77 0.76 -1.32E-02 0.130(0.047) W.Tura 12 11.81 115 54.12 5.01 0.73 0.75 2.67E-02 0.138(0.048) W.Murm 29 28.94 124 57.44 4.83 0.7 0.71 1.41E-02 0.161(0.053) W.Amur 20 20 111 52.76 4.53 0.71 0.7 -1.43E-02 0.154(0.058) W.Magad 6 6 89 43.31 4.44 0.71 0.68 -4.41E-02 0.134(0.075) W.Yakut 126 118.12 193 81.8 5.27 0.74 0.77 3.90E-02 0.151(0.024) W.Chuk 41 41 163 73.89 5.38 0.74 0.76 2.63E-02 0.154(0.043) Table 3. Private alleles by locus and sample Sample Locus Allele size [bp] Allelic Frequency Allelic count W.Yakut RT6 168 0.036 9 W.Yakut OHEQ 302 0.024 6 W.Yakut RT24 260 0.02 5 W.Yakut RT24 262 0.02 5 W.Yakut BMS1788 162 0.008 2 W.Yakut C32 314 0.008 2 W.Taym T40 335 0.045 2 W.Murm OHEQ 268 0.052 3 W.Chuk RT7 262 0.122 10 W.Chuk FCB193 124 0.049 4 W.Chuk BMS1788 176 0.024 2 W.Chuk FCB193 120 0.024 2 Todzha BMS745 131 0.071 6 Todzha OHEQ 322 0.024 2 Chukchi RT6 192 0.042 10 Chukchi BMS1788 166 0.008 2 deficiency a nd e xcess o f m icrosatellite heterozygotes. The mean Fis value in the pooled sample was 0.0023 ± 0.0071. Pairwise Fst values To assess the levels of genetic differentiation between the populations over all microsatellites, Nei’s pair- wise Fst values were computed (Figure 2, Supplemen- tal Table S8). According to a 5% significance level, the wild reindeer samples, except for ‘W.Yakut’ and ‘W.Chuk’, were genetically differentiated. The wild rein- deer from Chukotka differed only from the Amur wild population sample, and the wild reindeer from Yaku- tia were genetically similar to all sampled wild rein- deer populations. When comparing only domesticated reindeer, we found stronger genetic differentiation rela- tive to each other (all p-values < 0.03) than in group of wild reindeer. When comparing wild and domes- tic forms, we found significant differences (p-values < 0.05) for most (38 out of 42) pairs of populations, except for the pairs ‘W.Magad’–’Nenets’, ‘W.Magad’–’Chukchi’, ‘W.Yakut’–’Evens’ and ‘W.Yakut’–’Evenki’. The Tofalar and Todzha samples were the most distant from all other breeds and from each other (all p-values < 0.03). Wright’s F statistics for each locus For 16 loci, the overall inbreeding coefficient (Fit) varied from 0.0411 to 0.2296 (Table 4) with a mean of Genetic Resources (2022), 3 (6), 1–14 Genetic differentiation of reindeer populations 7 Figure 2. A visualization of pairwise Fst values. Colour intensity and size of circles are proportional to the pairwise Fst values between two population samples. Crosses indicate that no significant differences were found between two samples (p-value threshold = 0.05). The two black triangles separate domestic and wild reindeer forms. 0.0077. The highest Fit value reflecting a heterozygosity deficiency corresponded to marker C143, likely due to the small number of alleles at this locus (Cornuet and Luikart, 1996). The inbreeding coefficient, Fis, ranged from -0.0388 to 0.0977 with a mean of 0.0188, and the fixation index, Fst, ranged from 0.0486 to 0.1462 with a mean of 0.0808. Based on Fst, all loci (especially C143, RT30 and RT6) contributed to the differentiation between the herds. Bayesian clustering analysis To infer the population structure, Bayesian clustering was carried out using the Markov chain Monte Carlo methods. We performed 50 runs for each number of clusters from 1 to 13. We selected an admixture model with correlated allele frequencies. To find the optimal number of clusters, we performed simulations of 1,000,000 iterations with a burn-in period of 100,000 generations. For each animal, the genetic origin was assessed as the proportion of genetic membership to each cluster. Based on the statistic DeltaK with values of 216.98, 2.58, 520.04, 2.97, 0.14 and 0.72 for K = 2-6 and K13, respectively, we found the optimal number of clusters, (K = 4, DeltaK = 520.04) (Figure 3). The first cluster consists only of the Nenets breed (red bars), the second one includes Tofalar and Todzha ecotypes (green bars), the third one (purple bars) comprises the remaining three breeds (Evens, Evenki and Chukchi), while the fourth cluster combines all wild reindeer. At K = 13, when the maximum proportion of a sample’s membership in any of these clusters (Pm) is above 75%, we acknowledge ‘pure ancestry’ (Svishcheva et al, 2020). Pure ancestry was thus detected at four population samples: Tofalar (92.5%), wild Amur (87%), Todzha (82%) and wild Murmansk reindeer (79%). Other samples were found to have mixed ancestry. The 8 Svishcheva et al Genetic Resources (2022), 3 (6), 1–14 Table 4. Wright’s F statistics for each locus. Fit: the inbreeding coefficient of an individual relative to the entire population; Fst: the inbreeding coefficient of the subpopulation relative to the entire population expected under random mating; Fis: the inbreeding coefficient of an individual relative to its own subpopulation. Locus Number of alleles Fit Fst Fis BMS1788 28 0.109 0.078 0.034 BMS745 13 0.071 0.049 0.023 C143 3 0.23 0.146 0.098 C217 3 0.041 0.077 -0.039 C276 8 0.085 0.066 0.021 C32 9 0.118 0.085 0.037 FCB193 15 0.099 0.081 0.019 NVHRT16 13 0.047 0.052 -0.005 OHEQ 30 0.078 0.077 0.001 RT1 17 0.072 0.074 -0.002 RT24 17 0.117 0.084 0.037 RT30 15 0.163 0.131 0.037 RT6 17 0.087 0.091 -0.004 RT7 14 0.055 0.074 -0.02 RT9 14 0.081 0.08 0.001 T40 20 0.11 0.049 0.065 wild reindeer sample from Tura showed the lowest Pm values (< 20%). Phylogenetic analysis We reconstructed an unrooted phylogenetic tree by the neighbour-joining algorithm using Nei’s genetic distances (Figure 4). The domestic reindeer were grouped into two clusters with 100% bootstrap support (BS). The cluster highlighted in green contained only main breeds (BS = 100%), while the cluster highlighted in red included two ecotypes (BS = 100%). Among the wild populations, we did not identify any group (all BS < 45%). Isolation by distance analysis and centre of allelic richness We performed an isolation-by-distance (IBD) analysis based on the Mantel test. The original value of the correlation between genetic distances, measured as pairwise Fst/(1− Fst), and geographic (Euclid) distances is represented by the black dot (Figure 5a), and the histogram describes the permuted values in the absence of spatial structure. Since the original value is inside the reference distribution, the spatial structure is non- significant (p-value = 0.135, R2= 0.03). For the studied population samples, we theoretically determined the geographic region/centre of allelic diversity using their geographic coordinates as well as allelic richness and indices defining natural (climatic) zones as weights (see Supplemental Table S1). We estimated that this region is located in the north-western part of Yakutia (along the coast of the Laptev Sea), which is the core area of seasonal ranges and migration routes traditionally used by reindeer. We detected a significant negative correlation (cor = -0.57, p = 4.1×10−2) between the level of allelic richness in the population sample and the geographic distance from the sample to the theoretically calculated geographic centre (latitude ≈72◦, longitude ≈125◦). Figure 5b demonstrates the distribution density of population samples plotted using a bivariate kernel density estimate. Discussion In recent years, a difficult d emographic s ituation has developed in wild reindeer herds of some geographic regions of Eurasia due to the industrial development of the Arctic and adjacent territories, uncontrolled hunting, loss of grazing land and climatic changes (Kirpotin et al, 2021). A severe economic and cultural disruption has been observed in some herds of domesticated reindeer, such as Tofalar and Todzha ecotypes. These reindeer are bred by small Indigenous groups and mark the southern limits of reindeer husbandry. Although the reindeer is an extremely important species for the Indigenous Arctic ethnic groups living in the arctic and subarctic regions of Eurasia, information on the population structure of this species is limited. It is clear that genetic studies provide knowledge that can be useful in the conservation of genetic variation by identifying the intraspecific genetic structure and also in the management of wild hunting and poaching. The first s tep t o t his i s t o i dentify the population structure of this arctic species. We studied the genetic structure of Rangifer tarandus L. from the Eurasian part of the range. Our study included 13 population samples of animals, covering most of the range, six of which were domesticated to varying degrees, and the remaining seven samples belong to the wild form of reindeer from tundra, taiga and mountainous nature zones. The studied samples from wild reindeer populations relate to the different subspecies according to (Danilkin, 1999). The Murmansk reindeer selected from the western part of the range, namely from the Kola Peninsula, belongs to a subspecies R. tarandus tarandus; the Taymyr and Tura reindeer selected from the central part of the range, namely from the Taymyr Peninsula and Central Siberian Plateau, belong to a subspecies R. tarandus sibiricus; the Amur, Magadan, Yakutia and Chukotka reindeer samples selected from the eastern part of the range belong to a subspecies R. tarandus phylarchus. All studied wild populations of reindeer differ in morphological features. Our population structure analysis used a newly developed panel of 16 polymorphic microsatellites with high polymorphism information content. When analyzing allele content, we detected private alleles specific for four wild reindeer herds (‘W.Yakut’, ‘W.Taym’, ‘W.Murm’ and ‘W.Chuk’) and for two domes- ticated ones (‘Todzha’ and ‘Chukchi’), which suggests that each of these herds probably has formed a unique genepool under different (selection, geographical or feed) conditions. Besides, for the Todzha ecotype and Genetic Resources (2022), 3 (6), 1–14 Genetic differentiation of reindeer populations 9 Figure 3. Results of STRUCTURE analysis based on microsatellite genotypes. Colour indicates the proportion of membership of each animal to K assumed clusters. Figure 4. Phylogenetic tree constructed by the neighbour-joining algorithm. Numbers at the branch nodes indicate the confidence values for each branch obtained using the bootstrap procedure. The red and green rectangles show clusters with BS > 95%. 10 Svishcheva et al Genetic Resources (2022), 3 (6), 1–14 Figure 5. a) Histogram of simulated values. Results of Mantel test of correlations between genetic differentiation (Slatkin’s linearized Fst) and geographic distance (spatial Euclidean). The black dot represents the original value of the correlation between genetic distances, measured as pairwise Fst/(1− Fst), and geographic (Euclid) distances. b) Distribution density of population samples plotted using a bivariate kernel density estimate. The linear regression trend is shown with a dashed line; colours represent degrees of density (blue: low; yellow: medium; red: high density). the wild Murmansk reindeer, we found a high degree of ‘pure ancestry’, since the estimated maximum propor- tion of animal membership in these herds was 82.0% and 79%, respectively. Along with these two herds, we identified t wo m ore g enetically h omogeneous herds: the Tofalar ecotype (92.5%) and the wild Amur (87%) reindeer. For the Todzha and Tofalar samples, this is explained by a high degree of taming of reindeer due to taiga-specific (spatially limited) breeding and keeping conditions and, of course, indigenous traditions (Klokov, 2020). For the wild population samples from the Amur and Murmansk regions, ‘pure ancestry’ can be explained by their geographical remoteness from the crossings of major migration routes. By analyzing the heterozy- gosity and allelic richness, we suggest a comparatively low genetic variability in the Tofalar and Todzha eco- types, whereas a high genetic variability was observed in the wild herds from the Taymyr, Chukotka and Yakut regions. This is due to the large population sizes and long-distance migration capability of the wild reindeer herds compared to domestic Tofalar and Todzha rein- deer. It is interesting that according to the Fis index (Table 2), we observed no noticeable inbreeding in the studied populations. However, analysis of the distribu- tion of inbreeding coefficients calculated f or each ani- mal, based on maximum likelihood method, showed comparably high inbreeding in the isolated samples of Todzha and Tofalar reindeer (Supplemental Table S9). Results of Bayesian clustering analysis showed that all wild reindeer herds were clustered together. However, the samples of domesticated animals were distributed among the three clusters according to the degree of their domestication. As expected, the Todzha and Tofalar herds having the deepest level of taming, without seasonal migrations, formed a separate cluster, while the main breeds living in conditions of large herd formation, including long seasonal migrations (Stolpovsky et al, 2020), formed a cluster including only the Nenets breed and a cluster including the remaining breeds. The special genetic status of the domesticated Nenets reindeer is associated with the peculiarities of their historical formation (Dotsev et al, 2017) (Kharzinova et al, 2015). However, the phylogenetic analysis did not provide a high bootstrap support for joint clustering of wild herds and did not distinguish the Nenets breed from the rest, but confirmed the joint clustering of two ecotypes of reindeer, as well as the joint grouping of reindeer from three breeds (Evenki, Evens and Chukchi), adding to them the Nenets breed. The analysis of paired Fst confirmed the population genetic differentiation between domestic and wild forms of the Eurasian reindeer, as well as the genetic struc- turing within each form (Figure 3). When comparing these two forms, we found significant differences only for 4 out of 42 pairs of populations. We suppose that for pairs ‘W.Magad’–‘Nenets’, ‘W.Magad’–‘Chukchi’ this can be attributed to the small size of the Magadan sample (n = 6) versus the large sample sizes of the Chukchi (n = 120) and Nenets breeds (n = 228). The genetic isolation of tame reindeer from wild ones living in the same geographic region is also worth mention- ing. In particular, for the ‘W.Amur’–‘Evenki’ pair from Genetic Resources (2022), 3 (6), 1–14 Genetic differentiation of reindeer populations 11 the Stanovoy Range region and the ‘W.Chuk’–‘Chukchi’ pair from the Chukotka Mountains region we did not see the effect from an exchange of some individuals. The comparison of only the five population samples of the wild herds (‘W.Taym’, ‘W.Tura’, ‘W.Murm’, ‘W.Amur’ and ‘W.Magad’) showed that they are genetically dif- ferentiated from each other, but to a lesser extent than breed samples. The ‘W.Yakut’ sample has no genetic dif- ferences from all others, and ‘W.Chuk’ differs only from ‘W.Murm’ and ‘W.Amur’, which have characteristic feed- ing and behaviour patterns (Baskin, 1986). The wild reindeer from Yakutia and Chukotka belong to very large herds (with a high level of genetic diversity) that dur- ing seasonal migrations cover vast territories of the tun- dra and taiga. Probably, the exchange of genetic material between the wild reindeer herds takes place on overlap- ping migration routes. The population from Yakutia is especially distinguished in the group of samples of wild reindeer, which occupies the central part of the range relative to the studied populations, and has the maxi- mum level of genetic diversity. We showed that the num- ber of common alleles in ‘W. Yakut’ is about 82% of the total allelic diversity of the studied population samples, which significantly exceeds these indicators for the rest of the regional reindeer samples (Table 3). According to the results of our study, Yakutia is the centre of the allelic diversity of the genepool of the Rangifer tarandus L. species in the Eurasian part of the range. When com- paring only the domestic herds, we found a higher level of genetic differentiation than among wild reindeer. This fact is consistent with an ecologically determined selec- tion process that led to the emergence of divergent breeds in different geographic regions due to their dif- ferent adaptability. In particular, the isolated samples of the Todzha and Tofalar herds showed the highest level of differentiation from all the breed samples (Fst = 0.019- 0.091, p-values < 0.05), while a low but significant dif- ference was observed between the Evens and Chukchi breed samples (Fst = 0.014, p-value < 0.05), which can be explained by the fact that the breeds have been cre- ated on the basis of the same constantly migrating natu- ral populations of the eastern part of the reindeer range. Although the IBD analysis did not reveal a spatial structure of the population samples, which may be associated with long-term seasonal migrations of animals, we theoretically determined the geographic region of allelic diversity for the studied group of population samples. As expected, this region is located in the north-western part of Yakutia: the coast of the Laptev Sea, where the major seasonal ranges and migration routes pass. The Yakutsk, Taymyr and Chukotka wild populations were the geographically closest to the centre of allelic diversity. Among the factors that limit species distributions and effect on genetic structure, undoubtedly the last glacial maximum (LGM) has been the major force shaping reindeer geographic ranges in northern Eurasia. As it has been shown (Grosswald, 1999), in this part of the continent, exactly in the area of the Taymyr Peninsula, the LGM boundary has been discovered, extending to the northeast. However, the Laptev Sea region, which is part of West Beringia, has not been glaciated since at least the Tazov Glacial (Wetterich et al, 2011). Therefore, an eastern part of the species range has been significantly preserved. The species Rangifer tarandus L. has lived here both during the ice age and in the subsequent period of natural disasters caused by the melting of glaciers. The Pleistocene glaciation undoubtedly has influenced the genogeography of the species. This confirms the maximum genotypic diversity of reindeer in the eastern part of the range. From this region, resettlement has likely occurred throughout Eurasia range at the end of the LGM period. Long- term migrations have been inevitably accompanied by a loss of genetic diversity, a fact also known from studies of other species inhabiting this territory, for example, sable (Kashtanov et al, 2015). Conclusion Overall, statistical analysis of microsatellite data revealed a significant genetic differentiation between domestic and wild reindeer and confirmed popula- tion structures within each form. Our results high- light the importance of collecting microsatellite data from wild and domesticated reindeer in providing new insights into the genetic diversity and population struc- ture of reindeer, which can help design strategies for genetic conservation and improvement as well as sup- port resource use. Author contributions GRS: development of statistical methods, statistical analysis, interpretation of results, development of a concept, draft of the work, writing of the manuscript, final approval of the manuscript; SNK: interpretation of results, writing of the manuscript, final approval of the manuscript; OVB: microsatellite analysis, final approval of the manuscript; TPS: collecting of biological material, discussion of the results, final approval of the manuscript; MTS: DNA extracting; MVK: collecting of biological material, discussion of the results, final approval of the manuscript; YAS: development of a concept, draft of the work, collecting of biological material, interpretation of results, writing of the manuscript, final approval of the manuscript. Funding This research was supported by RSF grant no. 22-16- 00062. Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 12 Svishcheva et al Genetic Resources (2022), 3 (6), 1–14 Ethics statement All experiments were approved by the Local Ethics Committee of the Vavilov Institute of General Genetics of the Russian Academy of Sciences, Russia. The animal use procedures were carried out in accordance with the national veterinary guidelines. Data accessibility Microsatellite genotyping data was deposited in Zenodo: 10.5281/zenodo.6376769. Supplemental data Supplemental Table S1. Characteristics and geographic locations of the analyzed population samples of reindeer Supplemental Table S2. Description of microsatellite markers Supplemental Table S3. P-values of the Hardy–Weinberg equilibrium test with the FRD-based correction for each combination of sample and locus Supplemental Table S4. P-values of the Hardy–Weinberg equilibrium test for each locus Supplemental Table S5. The proportions of loci that are out of HWE for each population Supplemental Table S6. Population parameters calcu- lated for each locus and each population sample. Supplemental Table S7. The polymorphism informa- tion content values for each combination of sample and locus Supplemental Table S8. Nei’s pairwise Fst values among samples. Supplemental Table S9. The distribution of maximum-likelihood-based estimates of inbreeding coefficients References Agapow, P. M. and Burt, A. (2001). Indices of multilocus linkage disequilibrium. Molecular Ecology Notes 1, 101–103. doi: https://doi.org/10.1046/j.1471-8278. 2000.00014.x Baranova, A., Panchenko, D., Kholodova, M., Tirronen, K., and Danilov, P. (2016). Genetic diversity of wild reindeer Rangifer tarandus L. from the eastern part of the Kola Peninsula: Polymorphism of the mtDNA control region. 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