Dennis I. Litovka

Mitochondrial DNA is a powerful marker for reconstructing maternal lineages and phylogeographic history in wild species. The snow sheep (Ovis nivicola), a mountain ungulate endemic to Siberia and the Russian Far East, is characterized by strong geographic fragmentation and a complex taxonomic structure. A formal mitochondrial haplogroup classification system for the species has, however, been lacking. In this study, we sequenced and analyzed 257 complete mitochondrial genomes representing all major populations across the species’ range. Using phylogenetic reconstruction and diagnostic mutations (present in 100% of individuals within clades), we identified four major haplogroups (A–D) and multiple nested sub-haplogroups with clear geographic structuring. Divergence times were estimated using Bayesian analysis in BEAST 2.7.7 with a relaxed lognormal clock model, calibrated by the divergence between O. dalli and O. canadensis. The results showed that haplogroup A is almost exclusive to Kamchatka, haplogroup B is predominant in northern populations, haplogroup C is the most widespread and diverse across the species range, and haplogroup D is largely restricted to the Verkhoyansk region. The mitochondrial data largely support traditional subspecies taxonomy but reveal mitochondrial polyphyly in some subspecies, particularly O. n. lydekkeri and O. n. koriakorum. This study provides the first comprehensive mitochondrial haplogroup system for snow sheep and shows that the observed mitochondrial phylogeographic patterns are consistent with an important influence of Pleistocene glacial cycles and prolonged geographic isolation on its current genetic structure. These findings offer important insights for conservation management and for understanding the evolutionary history of wild sheep in Northeast Asia.

The snow sheep ( Ovis nivicola ) is a wild mountain artiodactyl inhabiting the highlands of northeastern Siberia and the Russian Far East. Despite its ecological and conservation importance, the intraspecific taxonomy of this species has long remained controversial due to its broad distribution, morphological variability and limited available material. In this study, we performed a comprehensive genome‐wide single nucleotide polymorphism (SNP) analysis, encompassing all major described populations to clarify the evolutionary relationships and taxonomic structure of snow sheep. A total of 337 individuals from 15 populations were genotyped using Illumina OvineSNP50 and OvineHD 600 K BeadChips, with 1032 autosomal SNPs retained after quality filtering. Population structure analyses revealed eight major genetic clusters largely corresponding to geographic regions: Kamchatkan, Koryak, Okhotsk/Chukotka, Yakutian (Moma Range), Yakutian (Verkhoyansk Range), Yablonov, Kharaulakh and Putorana. Pairwise F _ST values confirmed substantial differentiation among most populations. Using f ₃ ‐statistics, two significant admixture events were identified, one involving the Orulgan and another the northern Kamchatka populations. Based on the integrated genomic and historical evidence, our results support the taxonomic recognition of the Kamchatkan ( O. n. nivicola ), Putorana ( O. n. borealis ), Okhotsk ( O. n. alleni ), Koryak ( O. n. koriakorum ), Yakutian ( O. n. lydekkeri ), and Stanovoy/Yablonov ( O. n. potanini ) subspecies, and propose recognition of the Kharaulakh snow sheep as a new subspecies, O. n. ernsti . The Kodar population, characterized by extremely low heterozygosity, requires further sampling to resolve its taxonomic position. These findings provide the first genome‐wide framework for understanding the evolutionary history of snow sheep and establish a genetic basis for future conservation management.

The intraspecific taxonomy of snow sheep (Ovis nivicola) is one of the most controversial issues in Caprinae systematics. Although eight subspecies have been described using morphological traits, the validity of several taxa, particularly those in the eastern part of their geographical range, remains disputed. We investigated the phylogenetic relationships and genetic diversity of snow sheep in the Chukotka Autonomous Okrug using genome-wide single-nucleotide polymorphism (SNP) data (935 loci after filtering) and complete mitochondrial genomes from 57 individuals collected across the Russian Far East (Chukotka Autonomous Okrug, Kamchatka Krai, Magadan Oblast, and Republic of Sakha (Yakutia)). Post SNP-genotyping nuclear genomic studies using principal component analysis (PCA), ADMIXTURE, pairwise F_ST and Neighbor-Net revealed two clearly differentiated groups within Chukotka. One included individuals from the Koryak Mountains that are genetically indistinguishable from the Koryak subspecies (O. n. koriakorum) of northern Kamchatka. The other one encompassed individuals from the Anadyr Plateau and the Chukotka Mountains cluster with the Okhotsk subspecies (O. n. alleni) of the Kolyma Mountains. Bayesian phylogeny of complete mitochondrial genomes fully corroborated the nuclear results: Koryak Mountains samples formed a monophyletic clade, while Anadyr–Chukotka samples grouped with Kolyma Mountains individuals. Genetic diversity indices (_UH_E, A_R, F_IS) in both Chukotka groups were comparable to other studied populations and showed no signs of inbreeding depression. Our results provide important insights and can be used to develop science-based strategies for preserving the population-genetic diversity of snow sheep.

The Russian Arctic supports wild sympatric ruminants and their data-deficient helminths. In this study, we: (1) collected fecal samples of wild and semiwild reindeer (Rangifer tarandus), muskoxen (Ovibos moschatus), and snow sheep (Ovis nivicola) across Palearctic North territories: Arkhangelsk Oblast (including Novaya Zemlya archipelago), Karelia and Sakha Republics, Kola, Yamal, Taimyr, and Chukotka Peninsulas, Bering, Svalbard, and Wrangel Islands; (2) conducted a coprological survey (noninvasive life-time method preferable for protected animals) to obtain eggs and larvae of helminths inhabiting digestive, respiratory, nervous, and muscular systems; (3) identified helminths according to their morphology and DNA sequences; (4) estimated parasite load per host; (5) analyzed our findings. Varestrongylus eleguneniensis (in reindeer) was reported for the Palearctic for the first time, while Orthostrongylus sp. was reported both for R. tarandus and for the Palearctic for the first time. Capillarid-type eggs were reported for snow sheep for the first time. The question of the role of wild Arctic ruminants as vectors for rotifers was raised.

Recent findings that some gray whales that feed off Sakhalin Island (SI), Russia, in the western North Pacific (WNP) overwinter in the eastern North Pacific (ENP) indicate that population structure in this species is more complex than originally thought. We generated mitochondrial DNA (mtDNA) control region sequences and microsatellite genotypes (n = 12 loci) from 156 whales sampled off SI and compared them to available data from 106 ENP whales. Significant mitochondrial and nuclear genetic differentiation between the SI and ENP whales was found. Genetic cluster analysis identified two groups among the SI whales, one of which was genetically similar to ENP whales. Photographs collected from the biopsied SI whales showed that both groups comprised whales known to migrate to the ENP, suggesting that the clustering pattern was not reflective of some SI whales interbreeding while overwintering in the WNP. Instead, the genetic differentiation observed between the SI and ENP whales may be due to assortative mating of SI whales while west of eastern migratory routes. The rare but continued reports of gray whales off the coasts of Japan and China, however, confirm that some gray whales overwinter in the WNP and highlight the need to collect additional data from these whales.

At least five populations (stocks) of beluga whales (Delphinapterus leucas) are thought to winter in the Being Sea, including the Bristol Bay, Eastern Bering Sea (Norton Sound), Anadyr, Eastern Chukchi Sea, and Eastern Beaufort Sea (Mackenzie) populations. Belugas from each population have been tagged with satellite‐linked transmitters, allowing us to describe their winter (January–March) distribution. The objectives of this paper were to determine: (1) If each population winters in the Bering Sea, and if so, where? (2) Do populations return to the same area each year (i.e., are wintering areas traditional)? (3) To what extent do the winter ranges of different populations overlap? Tagged belugas from all five populations either remained in, or moved into, the Bering Sea and spent the winter there. Each population wintered in a different part of the Bering Sea and populations with multiple years of data (four of five) returned to the same regions in multiple years. When data were available from two populations that overlapped in the same year, they did not occupy the shared area at the same time. Although our sample sizes were small, the evidence suggests belugas from different populations have traditional winter ranges that are mostly exclusive to each population.

The frequency of occurrence of the mitotypes (control region, cytochrome b gene, and DN2 gene) has been studied for groups of gray whales feeding and growing along Chukotka Peninsula, Koryak Coast, eastern Kamchatka, and Sakhalin Island. The number of the mitotypes decreased dramatically from the northern waters southwards; however, the dominant mitotypes remained the same. Both mitochondrial lineages known for this species might be found for the whales gathering in the reproductive area along the Californian Coast in accordance with the comparison of the published and original data on the haplotypes of the control region. However, it has also been argued that similar sequences of the control region might be found in different mitochondrial genomes, and the analysis of only this site of mtDNA might lead to incorrect conclusions.

Arctic marine mammals (AMMs) are icons of climate change, largely because of their close association with sea ice. However, neither a circumpolar assessment of AMM status nor a standardized metric of sea ice habitat change is available. We summarized available data on abundance and trend for each AMM species and recognized subpopulation. We also examined species diversity, the extent of human use, and temporal trends in sea ice habitat for 12 regions of the Arctic by calculating the dates of spring sea ice retreat and fall sea ice advance from satellite data (1979-2013). Estimates of AMM abundance varied greatly in quality, and few studies were long enough for trend analysis. Of the AMM subpopulations, 78% (61 of 78) are legally harvested for subsistence purposes. Changes in sea ice phenology have been profound. In all regions except the Bering Sea, the duration of the summer (i.e., reduced ice) period increased by 5-10 weeks and by >20 weeks in the Barents Sea between 1979 and 2013. In light of generally poor data, the importance of human use, and forecasted environmental changes in the 21st century, we recommend the following for effective AMM conservation: maintain and improve comanagement by local, federal, and international partners; recognize spatial and temporal variability in AMM subpopulation response to climate change; implement monitoring programs with clear goals; mitigate cumulative impacts of increased human activity; and recognize the limits of current protected species legislation.

This article presents data on frequencies of mitotypes (control region and cytochrome b and ND2 genes) in groups of Gray Whales found off the Chukotka Peninsula, Koryak coast, eastern Kamchatka and Sakhalin Island. From north to south the number of mitotypes decreased dramatically, but mitotypes which were predominant in the south were the same as ones also abundant in northern samples. For the control region sequences only, our data and data presented in the literature suggest that breeding areas in Baja California may include diversity of both distant groups of mitochondrial lineages known for the species. On the other hand, the same control region sequences may be found in different mitochondrial genomes, and so conclusions based on this mtDNA fragment only may be incorrect.