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Demographic history and divergence of sibling grouse species inferred from whole genome sequencing reveal past effects of climate change



The boreal forest is one of the largest biomes on earth, supporting thousands of species. The global climate fluctuations in the Quaternary, especially the ice ages, had a significant influence on the distribution of boreal forest, as well as the divergence and evolution of species inhabiting this biome. To understand the possible effects of on-going and future climate change it would be useful to reconstruct past population size changes and relate such to climatic events in the past. We sequenced the genomes of 32 individuals from two forest inhabiting bird species, Hazel Grouse (Tetrastes bonasia) and Chinese Grouse (T. sewerzowi) and three representatives of two outgroup species from Europe and China.


We estimated the divergence time of Chinese Grouse and Hazel Grouse to 1.76 (0.46–3.37) MYA. The demographic history of different populations in these two sibling species was reconstructed, and showed that peaks and bottlenecks of effective population size occurred at different times for the two species. The northern Qilian population of Chinese Grouse became separated from the rest of the species residing in the south approximately 250,000 years ago and have since then showed consistently lower effective population size than the southern population. The Chinese Hazel Grouse population had a higher effective population size at the peak of the Last Glacial Period (approx. 300,000 years ago) than the European population. Both species have decreased recently and now have low effective population sizes.


Combined with the uplift history and reconstructed climate change during the Quaternary, our results support that cold-adapted grouse species diverged in response to changes in the distribution of palaeo-boreal forest and the formation of the Loess Plateau. The combined effects of climate change and an increased human pressure impose major threats to the survival and conservation of both species.


To understand the possible effects of on-going and future climate change it would be useful to reconstruct past population size changes and relate such to climatic events in the past. Analyses of whole genome sequences can be used to infer past demographic events and has been used in the past to infer effects of major climatic events in the evolutionary past of study organisms [1,2,3]. Here we use whole genome sequences obtained from individuals belonging to a pair of boreal forest dwelling sibling grouse species: the Chinese Grouse Tetrastes sewerzowi and Hazel Grouse T. bonasia to infer their demographic histories and relate the inferred changes to known past climatic geological events and their climatic impacts [4]. The demographic history of a species can be examined through reconstruction of the effective population size, using the information embedded in the genome sequences of a diploid species [1].

The boreal forest, one of the largest biomes on Earth, is inhabited by a large number of species [5,6,7]. Most of the boreal forest is found throughout the high northern latitudes from about 50°N to 70°N, but boreal forest also occurs in mountain regions further to the south, such as at the southeastern edge of the Qinghai–Tibet Plateau (QTP). These forest areas used to be connected to the more widely distributed boreal forest to the north, but retracted to present distributions during the uplifting of the plateau [8]. The global climate change and uplift of QTP had significant influence on divergence and demographic history of many sibling faunas in Eurasia and the QTP boreal forest. Hazel Grouse and Chinese Grouse, the sibling species of concern in this study show adaptions to cold environments, for example by having feathered legs and nostrils [9,10,11]. The origin of the cold-adapted Pleistocene species, especially megafauna, has usually been sought either in the arctic tundra or in the cool steppes outside the QTP [12, 13]. However, an alternative scenario, called the “out of Tibet hypothesis”, has been offered, based on new fossil assemblages [14,15,16]. Here it is argued that the evolution of present-day animals in the Arctic region is intimately connected to ancestors that first became adapted for life in cold regions in the high-altitude environments of the QTP (2.6 to 5 Mya), and were thus pre-adapted to cold climates during the ice age (2.6 to 0.1 Mya) [14,15,16].

During the Quaternary, vast areas of the boreal forest were repeatedly affected by major glaciations. Ice sheets that formed over Scandinavia spread eastwards across the northwestern Russian plains and southwards across northern and middle Siberia [17]. However, the ice sheets did not cover eastern Siberia. This led to a fauna in eastern Siberia that is considerably older and richer, with more endemic species and genera from various taxa, than that of the western Siberian boreal forest [17,18,19,20,21,22]. The historical glacial-interglacial cycles dramatically influenced the fluctuations and long-term declines in effective population size for some species [2, 23,24,25]. Therefore, the glaciations had a significant influence on the present genetic structure of populations, species, and communities [22, 26, 27].

Before the Quaternary Period, the global average temperature in the mid-Pliocene (3.3–3 Mya) was 2–3 ℃ higher than today, whereas carbon dioxide levels were the same [28]. The change to a cooler, drier, and more seasonal climate had considerable impacts on the Pliocene vegetation, reducing tropical species worldwide, and coniferous forests and tundra covered much of the north [29]. During the Late Miocene and the Pliocene periods, the QTP had an expanding forest cover (up to between 1000 and 2000 m) [30,31,32,33], particularly at its eastern edge, currently located in the Chinese provinces of Qinghai, Sichuan, and Yunnan. During the Quaternary period, the boreal forest experienced many climate-induced fluctuations [7]. Several glacial and interglacial periods, along with the uplift of the QTP and accumulation of the Loess Plateau, promoted the geographic distribution pattern of boreal forests seen today.

During the Pleistocene-Holocene (1.10–0.60 Ma), the QTP experienced three rapid uplifting stages when mountain ranges formed. The climate changed from wet and warm to dry and cold, with cryosphere (year round ice) on the plateau and forest receding to the edge. About 0.15 Ma, the climate became colder and drier, and 10 kya the forests extended even higher [34, 35]. The rise of the QTP and increase in global ice volume had a great influence on the interior Asian aridification, especially for the formation of the Loess Plateau from sediment deposited by wind-blown dust since 2.40 Ma [36,37,38,39]. In the early Quaternary, the warm and moist forest ecological environment found in the Tertiary continued on the Loess Plateau. During the middle and late Pleistocene, the climate gradually became drier and the vegetation changed to an arid grassland. These patterns are not only consistent with the changing characteristics of the fossil assemblage, but are also consistent with the formation of the loess area [40,41,42].

The Chinese Grouse is an endemic species inhabiting high mountain coniferous forests in central China [4, 43]. The Hazel Grouse is the sibling species of Chinese Grouse and occurs within the temperate, boreal, and subarctic biogeographical zones of the Northern Hemisphere [44, 45]. Studies on speciation of the sibling species show conflicting patterns [9, 46, 47], but previous work support a divergence in the mid-Pleistocene and the beginning of the Quaternary. Here we used whole genome resequencing data to estimate the divergence time and used a pairwise sequentially Markovian coalescent (PSMC) modeling to reconstruct the ancestral demographic trends in both species. Finally, we used a multiple sequentially Markovian coalescent (MSMC) model to estimate the effective population size of both species in more recent time.


Phylogenetic relationships

The trees based on the three different methods (Bayesian, Maximum Likelihood and Neighbor Joining) used to infer the phylogenetic relationships largely concur and only show minor differences among the different populations of Hazel Grouse (Fig. 1, Additional file 1: Figs. S1, S2). In the Bayesian and ML analyses, the China and GER/POL populations are sister to each other and in the NJ analyses the GER/POL and SWE populations are sister to each other. All analyses suggest clear species and population structure. Using the Bayesian tree, the split between the Chinese Grouse and Hazel Grouse was estimated to 1.76 (0.46–3.37) MYA. In all analyses the three populations of Chinese Grouse were clearly separated and the branching patterns was the same among populations. The Swedish population appeared to be clearly separated from the populations in Germany and Poland. Thus the broad phylogenetic relationships among the sampled species were independent of phylogenetic reconstruction method, as maximum-likelihood and Neighbor joining methods yielded the same topologies (Fig. 1, Additional file 1: Fig. S1, S2).

Fig. 1
figure 1

Phylogenetic relationships based on Bayesian analyses. The split between these sibling species is given in million years (with 95% confidence limits) from MSMC analysis. Numbers at the nodes indicate posterior probabilities

Population demographic histories

An analysis of full diploid genomes from the sibling species using PSMC modelling clearly showed different results for the two species (Fig. 2). For the Chinese Grouse, the southern (LHS and ZN) and northern populations (QLS) had different demographic histories. The northern population experienced persistently low effective population sizes and has declined since about 0.2 Mya. The demography of the southern Chinese Grouse population showed a drastic decline around 17,000 years ago and population peak at 20–30,000 years ago (Fig. 2). For the Hazel Grouse, the population peaked around 120,000 years ago (Fig. 2). Since 100,000 years ago the population trends differed between the Chinese and European Hazel Grouse populations (Fig. 3). All the European populations appeared to have undergone persistent declines in population size after the peak 0.3 Mya. In contrast, population size estimates obtained from the Chinese Hazel Grouse samples showed an increase to a large effective population size during the Last Glacial Maximum (LGM) 20,000 years ago. Since then, Hazel Grouse in China showed consistently higher population sizes than European Hazel Grouse (Fig. 3).

Fig. 2
figure 2

Historical changes in effective population size of Chinese Grouse and Hazel Grouse from PSMC analyses of whole-genome sequences. Profiles labeled SWE and GER, the Europan populations, (green) are from Hazel Grouse, ZN (olive), LHS (blue) and QLS (red) are from Chinese Grouse

Fig. 3
figure 3

Historical changes of effective population size in Hazel Grouse from PSMC analyses of whole-genome sequences. Profiles SWE (green) are from Jämtland, Sweden, XLJ (blue) are from China and GER/NEP (red) are of German/Polish ancestry

The MSMC analysis of the demographic history of the two grouse species from ~ 20,000 years ago to the present suggested that population sizes have decreased constantly since then to 2000 years ago with a decline in Chinese Hazel Grouse starting from higher levels than the European populations (Fig. 4).

Fig. 4
figure 4

Effective population size of Chinese Grouse (left) and Hazel Grouse (right) inferred using Multiple Sequentially Markovian coalescent (MSMC) models


We have performed the first population-scale, whole genome sequencing studies of the sibling species Chinese Grouse and Hazel Grouse, inhabiting the QTP and Eurasian boreal forests, respectively. The analyses presented here provide insights into the phylogenetic relationships, ancestral population demography and recent time population decreases in relation to changes in climate and related factors in different populations of these two species. Our PSMC analyses covered a period from 1 Mya to 20,000 years ago (Fig. 2), a period covering the chronological distribution of four grouse genera (Tetrao, Bonasa, Lagopus and Dendragapus) [44].

Our whole genome data allowed us to estimate relationships between Chinese Grouse and Hazel Grouse and among different populations of the two sibling species. We estimated the divergence time of the two Tetrastes species to 1.7 (0.46–3.37) Mya using whole genome data, whereas a previous study estimated the same divergence to approximately 0.8–2.5 Mya based on ultra-conserved element sequences [9]. In the Pliocene period (5.33–2.58 Mya), the climate was similar to the present [28]: a cool, dry, seasonal climate, which had considerable impacts on the vegetation. All grouse species occur within the temperate, boreal, and Arctic biogeographical zones of the Northern Hemisphere and are adapted to cold climates [44]. The extinct Bonasa praebonasia is ancestral to the sibling species Chinese Grouse and Hazel Grouse [4, 43, 46]. Prior to the Quaternary period it is assumed that, B. praebonasia populations spread throughout the existing coniferous forests and crossed into the QTP. In the beginning of the Quaternary, with cyclic growth and decay of continental ice sheets [48, 49], the distribution patterns of boreal forests and steppes changed worldwide. The uplifting of the QTP and the continuous accumulation of loess in the Loess Plateau was the main driving factor to limit the distribution of boreal forest in China and divide the ancestral species in to sibling species.

The conifer-dominated forests with deciduous trees on the southeastern edge of QTP, in which only the wetter northern slopes have forest vegetation, resulted in fragmentation of the habitat for Chinese Grouse [43]. It has been shown that large-scale deforestation, intensive livestock grazing, and climate change exacerbates habitat loss and fragmentation [50,51,52,53]. Similar to other mountainous species [54, 55], the distribution of Chinese Grouse would likely have shifted in elevation as the coniferous forest changed under different climate change scenarios [3, 52]. However, unlike species that have greater dispersal capabilities, the Chinese Grouse has had limited to no contact with Hazel Grouse [56] and the treeless Loess Plateau has acted as a barrier to gene flow among the Northern Taiga and the Northeastern QTP.

The sequenced QLS birds were from the Qilian Mountains, which are situated on the northeastern edge of the QTP; the coniferous forests there are isolated from the southern QTP. Several rare and endemic gallinaceous bird species are found in the Qilian Mountains [57, 58]. Our results show that the northern population of Chinese Grouse (QLS) has a demographic history different from those in the south, probably as a consequence of isolation and small effective population sizes throughout their history. This patterns is especially visible during the Last Glacial Period (LGP, 115,000–11,700 years ago), where the pattern between the northern QLS population and the two southern populations (LHS and ZN) are different (Figs. 1 and 2). Based on our data, these populations have been split for a long time. Thus, gene flow among the southern populations and northern population is extremely unlikely.

The Qilian Mountains has served as a refuge, not only for the Chinese Grouse, but also for other gallinaceaous birds during the Pleistocene glaciations, such as the Blood Pheasant (Ithaginis cruentus), Chukar Partridge (Alectoris chukar), and Tibetan Partridge (Perdix hodgsoniae). These forest specialist gallinaceaous birds gradually withdrew from large parts of northern China as a consequence of the plateau uplift, the formation of the Loess Plateau, and loss of forest in these areas. Populations remained in isolated refugia (e.g. QLS) during the glaciations that occurred during the Quaternary glacial period [59]. In contrast to the Chinese Grouse from the Qilian Mountains, the two southern populations have similar PSMC profiles and are more closely related (Fig. 2). The effective population size in these two populations showed a peak during the LGM and then decreased. This supports the notion that the LGM had a less dramatic effect on these populations than on forest species in Europe and North America and is a distinct regional feature [60].

The Hazel Grouse inhabits dense coniferous forest, preferably with spruces (Abies) mixed with deciduous trees like Alnus, Betula, and Salix, and has limited dispersal capability and narrow habitat requirements [4]. It is a sedentary species and its distribution range is continuous across northern Eurasia from Hokkaido in the east to Western Europe in the west. It is classified as Lower Risk (least concern) by the IUCN [53], but red-listed in some central and southern European countries [4]. Many remaining Hazel Grouse populations are scattered and small, because of forest fragmentation and limited dispersal [61]. Our population sampling of this species includes samples from China, Sweden, and central Europe (Fig. 5). Our results suggest that the Chinese population is genetically distinct from the Swedish and the Central European populations, probably as a result of isolation by distance and accelerated genetic drift in smaller and more isolated populations in Europe [62].

Fig. 5
figure 5

Distribution and sampling localities of the Chinese Grouse (red) and the Hazel Grouse (blue). The fine scale distribution of Chinese Grouse (green) is inset in the lower left corner. The distribution area was downloaded from “The IUCN Red List of Threatened Species, which is not protected by copyright,” (IUCN 2020)

The Pleistocene environment and vegetation in Eurasia are important factors to take into account when discussing molecular divergence among Hazel Grouse [27, 63]. The PSMC results show that population size of European Hazel Grouse populations decreased since 120,000 years ago and the population was constantly at low numbers until 12,000 years ago, a time period which also coincides with the LGP. During the LGP, the ice sheet stretched from the northern parts of the British Isles, Germany, Poland, and the Taymyr Peninsula in western Siberia, with the deepest ice sheet over Scandinavia [64]. In contrast, northeastern Siberia was not covered by a continental-scale ice sheet [65] and may have served as a refugium for Hazel Grouse and other forest species. Thus, Chinese populations of Hazel Grouse remained relatively large during the LGP. Our results on Hazel Grouse are supported by studies showing that the fauna in the eastern Siberian boreal taiga is older and richer than that in western Siberia [18,19,20,21].

Our MSMC results suggest that after the LGP, Ne decreased dramatically from 10,000 years ago to the present for both sibling species. Additionally, the MSMC analysis of the sibling grouse species from ~ 20,000 years ago to the present suggested that population sizes have decreased constantly from 20,000 years ago to 200 years ago. The dramatic decreases in Ne from 10,000 years ago to the present, coincide with the climate change in the Holocene (10,000 years ago to 5000 years ago) and increasing human forest exploitation from 8500 years ago [66]. In the early Holocene, around 10,000 years ago, the interglacial period was firmly established, exhibiting a warmer and moister climate than today. The ice cover regressed and trees were able to re-colonize the northern taiga. However, at the beginning of the Neolithic Revolution (8500 years ago), humans started to exploit forests, not only for wood and food, but also for space [67, 68]. Nowadays, boreal ecosystems world-wide are threatened by direct human activity and climate change [7].


In conclusion, our analyses provided insights into divergence and demographic history of a sibling species pair both residing in cold boreal forest habitats in Eurasia and the QTP. Combined with the uplift history and reconstructed climate change during the Quaternary, our results support that cold-adapted grouse species diverged in response to changes in the distribution of palaeo-boreal forest and the formation of the Loess Plateau. Our analyses also provide insights in to how these sibling species have responded to changes in climate throughout their evolutionary history. In a recent study [3] we were able to show how the changes in population size of Chinese Grouse were related to climate induced shifts in the distribution of available habitat. Combined with the results of this study, they provide evidence of population resilience in the face of dramatic climatic fluctuations. However, smaller and fragmented populations are facing higher risks of loss of genetic diversity and extinction as exemplified by the Qilian population of Chinese Grouse. Our results also hint at anthropogenic induced stress in recent times, and thus unrelated to major climatic shifts, has reinforced the population declines of both species. The combined effects of climate change an increased human pressure thus impose major threats to the survival and conservation of both species.


Sampling and data collection

A total of 29 samples from the two sibling grouse species were collected from 8 locations; 16 Chinese Grouse samples from three populations from Gansu, China (3 from Beishan in the Qilian Mountains (QLS); 3 from Zhuoni County (ZN); 10 from the Lihanhuashan National Nature Reserve (LHS)) along with 13 Hazel Grouse samples from five populations (1 from Northeastern Poland (NEP); 1 from the Austrian Alps; 3 from the Bavarian forest, Germany (GER); 3 from Jämtland, Sweden (SWE); and 5 from Northeastern China (XLJ)) (Fig. 5, Additional file 1: Table S1). All the Chinese Grouse samples and the blood samples of Hazel Grouse from the XLJ population were collected in the field. The Hazel Grouse samples labeled F3 (with ancestry in NE Poland) and MR (with ancestry from the Austrian Alps) came from a captive stock at a breeding station in Germany. All other Hazel Grouse were obtained as muscle tissue from hunted individuals. We also used 3 Lagopus samples as an outgroup (2 Rock Ptarmigan L. muta and 1 Willow Grouse L. lagopus). All samples were collected and preserved in 99% ethanol and stored at −20 °C.

Resequenced genomes

All samples were sequenced using the Illumina sequencing platform (Illumina PE 150, Hi-Seq 4000) at Annoroad Gene Technology (Beijing, China). We used a Gentra Puregene Blood kit (Qiagen) to extract the genomic DNA from all samples according to the manufacturer’s (Illumina) Instructions. Then we assessed the quality of DNA by electrophoresis on 1% agarose gel and the quantity of DNA by a BioDrop mLITE spectrophotometer (a total of 15 mg of DNA was quantified using the spectrophotometer). Our analyses were based on cleaned reads, which were filtered following a three-step procedure: (1) removal adapter polluted reads > 5 bp, (2) removing low-quality reads with quality score < 19, and (3) sequence reads where Ns comprised > 5% were removed (additional file 2 Data filter summary and distribution). After filtering, a total of 686.04 Gb (97%, of 705.13 Gb) high-quality paired reads remained for further analysis.

Trimmomatic v0.33 was used to trim the Illumina fastq files and remove adapters, based on manufacturer’s adapter sequences. Raw data of fastq format were then processed with in-house perl scripts. In this step, clean data were obtained by removing reads containing adapter, reads containing poly-N, and low-quality reads from raw data. At the same time, Q20, Q30, and GC content of the clean data was calculated. All the downstream analyses were based on the clean data with high quality (Q30). The clean reads were mapped to the Chinese Grouse reference genome that we assembled [3] by BWA 0.7.5a [69], with parameters: aln -o 1 -e 10 -t 4 -l 32 -i 15 -q 10, and reads having a mean of approximately 15 × depth for each individual and > 90% coverage of the Chinese Grouse genome were retained for SNP calling. We employed a Bayesian algorithm in Samtools 0.1.19 [70] to call SNPs using the command ‘mpileup’ with parameters as ‘-q 1 -C 50 -S -D -m 2 -F 0.002 -u’. We calculated the genotype likelihoods from reads for each individual at every genomic location and estimated the allele frequencies. We used GATK version 3.2–2 [71] to call variations including SNPs and indels. We filtered SNPs using VCFtools v0.1.11 [72] and by following the criteria: coverage depth ≥ 4 and ≤ 1000 (which nearly twice of the average sequencing depth 16 × 32 sample); root mean square (RMS) mapping quality ≥ 20; the distance of adjacent SNPs ≥ 5 bp (this will exclude potential misaligned nucleotides); the distance to a gap ≥ 5 bp; reads mapping quality value ≥ 30 (this will exclude the reads misallocated to different site over the genome). SNPs with minor allele frequency (MAF) value under 0.05 were excluded with vcftools -maf 0.05 (this help us to exclude the SNPs of low frequency in the population for the common procedure of population genetics research). Haplotype missing call rate ≥ 0.05 were also excluded by in-house perl script (to get high call rate of SNPs among each individuals).

Phylogenetic trees construction

To estimate phylogenetic relationships, all SNPs were used to calculate the pairwise genetic distances among all samples. A neighbor-joining (NJ) tree with 100 bootstrap replicates was inferred using TreeBeST 1.9.2 [73] using all samples. Whole genome sequences from one Willow Ptarmigan from Newfoundland, Canada, and two Rock Ptarmigan from western Greenland were included as an outgroup. RAxML version 8.2.10 was used to infer phylogenetic trees with the maximum likelihood (ML) method based on the same dataset. MrBayes 3.2.7a × 86_64 was used to perform Bayesian inference of phylogeny with the parameters: lset nst = 6 rates = gamma, Ngen = 800,000 Samplefreq = 10 Printfreq = 100, sump burninfrac = 0.2, sumt burninfrac = 0.2. A MrBayes consensus tree was extracted with posterior probability supporting rates. MCMCtree contained in the PAML software provided Bayesian methods to estimate divergence times of genomic-sized sequences between populations and species [74]. When the average standard deviation of split frequencies went under 0.01 which means convergence, the burnin was set to (ngen/samplefreq) * 0.25 and then sumt was used to get the convergence tree.

Demographic history inferred from PSMC and MSMC

Demographic history reconstruction using a PSMC model can estimate changes in effective population size between 20 kya and 3 Mya [1]. Because the power of PSMC is quite limited for predictions more recent than 20 kya, MSMC analyses can be performed to recover more recent trends in effective population using multiple genome sequences [1, 75]. The demographic history of the two grouse species was inferred using PSMC modelling, as described in Li and Durbin [1], and MSMC analyses [75]. To run PSMC and MSMC, we assumed two parameters: the generation time (2.5 years) [43] and the mutation rate per generation (0.3 × 10–8 per nucleotide per year) [76]. The mutation rate (per nucleotide per year, μ) was selected from studies of Willow Grouse (0.299 × 10–8) and Rock ptarmigan (0.310 × 10–8) [76, 77]. PSMC was run for 100 iterations using an initial h = q ratio of 5 and the default time patterning. Bootstrapping was performed according to Li and Durbin [1] by resampling 500,000 bp chunks of the genome with replacement to perform 100 bootstrap replicates.

Availability of data and materials

Sequencing data for the Chinese Grouse and Hazel Grouse have been deposited in Short Read Archive under Project Number PRJNA588719 and PRJCA005913.



Qinghai–Tibetan Plateau


Species Distribution Modelling


Pairwise Sequentially Markovian Coalescent


Multiple sequentially Markovian coalescent


Maximum likelihood




Last glacial maximum


Last glacial period


Last interglacial


  1. Li H, Durbin R. Inference of human population history from individual whole-genome sequences. Nature. 2011;475(7357):493.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kozma R, Lillie M, Benito BM, Svenning JC, Höglund J. Past and potential future population dynamics of three grouse species using ecological and whole genome coalescent modeling. Ecol Evol. 2018;8(13):6671–81.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Song K, Gao B, Halvarsson P, Fang Y, Jiang Y-X, Sun Y-H, Höglund J. Genomic analysis of demographic history and ecological niche modeling in the endangered Chinese Grouse Tetrastes sewerzowi. BMC Genomics. 2020;21(1):1–9.

    Article  Google Scholar 

  4. Storch I. Conservation status and threats to grouse worldwide: an overview. Wildl Biol. 2000;6(4):195–205.

    Article  Google Scholar 

  5. Potapov P, Yaroshenko A, Turubanova S, Dubinin M, Laestadius L, Thies C, Aksenov D, Egorov A, Yesipova Y, Glushkov I. Mapping the world’s intact forest landscapes by remote sensing. Ecol Soci. 2008;13(2):51.

    Article  Google Scholar 

  6. Brandt J, Flannigan M, Maynard D, Thompson I, Volney W. An introduction to Canada’s boreal zone: ecosystem processes, health, sustainability, and environmental issues. Environ Rev. 2013;21(4):207–26.

    Article  Google Scholar 

  7. Gauthier S, Bernier P, Kuuluvainen T, Shvidenko A, Schepaschenko D. Boreal forest health and global change. Science. 2015;349(6250):819–22.

    Article  CAS  PubMed  Google Scholar 

  8. Cheng T-s. The fauna and its evolution of territorial vertebrates at Qinghai-Tibet plateau. Rep Beijing Nat Mus. 1981;9:1–17.

    Google Scholar 

  9. Persons NW, Hosner PA, Meiklejohn KA, Braun EL, Kimball RT. Sorting out relationships among the grouse and ptarmigan using intron, mitochondrial, and ultra-conserved element sequences. Mol Phylogenet Evol. 2016;98:123–32.

    Article  CAS  PubMed  Google Scholar 

  10. Baba Y, Siegfried K, Sun Y-H, Fujimaki Y. Molecular phylogeny and population history of the Chinese grouse and the hazel grouse. Bull Graduate School Social Cult Stud Kyushu Univ. 2005;11:77–82.

    Google Scholar 

  11. Li X-J, Huang Y, Lei F-M. Complete mitochondrial genome sequence of Bonasa sewerzowi (Galliformes: Phasianidae) and phylogenetic analysis. Zool Syst. 2014;39(3):359–71.

    Google Scholar 

  12. Kahlke R-D. The origin of Eurasian mammoth faunas (Mammuthus–Coelodonta faunal complex). Quatern Sci Rev. 2014;96:32–49.

    Article  Google Scholar 

  13. Kahlke RD. The history of the origin, evolution and dispersal of the Late Pleistocene Mammuthus-Coelodonta faunal complex in Eurasia (large mammals): Mammoth Site of Hot Springs; 1999.

  14. Deng T, Wang X, Fortelius M, Li Q, Wang Y, Tseng ZJ, Takeuchi GT, Saylor JE, Säilä LK, Xie G. Out of Tibet: pliocene woolly rhino suggests high-plateau origin of Ice Age megaherbivores. Science. 2011;333(6047):1285–8.

    Article  CAS  PubMed  Google Scholar 

  15. Wang X, Tseng ZJ, Li Q, Takeuchi GT, Xie G. From ‘third pole’to north pole: a Himalayan origin for the arctic fox. Proc R Soc B Biol Sci. 2014;281(1787):20140893.

    Article  Google Scholar 

  16. Wang X, Li Q, Takeuchi GT. Out of Tibet: an early sheep from the Pliocene of Tibet, Protovis himalayensis, genus and species nov. (Bovidae, Caprini), and origin of Ice Age mountain sheep. J Vertebrate Paleontol. 2016;36(5):1169190.

    Article  Google Scholar 

  17. Batchelor CL, Margold M, Krapp M, Murton DK, Dalton AS, Gibbard PL, Stokes CR, Murton JB, Manica A. The configuration of Northern Hemisphere ice sheets through the Quaternary. Nat Commun. 2019;10(1):1–10.

    Article  CAS  Google Scholar 

  18. Malyshev L, Peshkova G. Rare plants of Central Siberia. In: Novosibirsk: Nauka; 1979.

  19. Kurnaev S. Forest regionalization of the USSR. Map (1: 16,000,000) Sheet. 1990; 15.

  20. Goljakov P. Sosudistye rastenia Olekminskogo zapovednika [Vascular plant of Olekinskij Zapovednik]. Annotirovannyj spisok vidov. Flora and fauna of zapovednikov, vol. 54. In: Nauka, Moscow; 1994.

  21. Korolyuk AY, Zverev A. Database of Siberian vegetation (DSV). Biodiv Ecol. 2012;4(4):312.

    Google Scholar 

  22. Svendsen JI, Alexanderson H, Astakhov VI, Demidov I, Dowdeswell JA, Funder S, Gataullin V, Henriksen M, Hjort C, Houmark-Nielsen M. Late quaternary ice sheet history of northern Eurasia. Quat Sci Rev. 2004;23(11–13):1229–71.

    Article  Google Scholar 

  23. Zhao S, Zheng P, Dong S, Zhan X, Wu Q, Guo X, Hu Y, He W, Zhang S, Fan W, et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat Genet. 2013;45(1):67–71.

    Article  CAS  PubMed  Google Scholar 

  24. Hung C-M, Shaner P-JL, Zink RM, Liu W-C, Chu T-C, Huang W-S, Li S-H. Drastic population fluctuations explain the rapid extinction of the passenger pigeon. Proc Natl Acad Sci. 2014;111(29):10636–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mays HL, Hung C-M, Shaner P-J, Denvir J, Justice M, Yang S-F, Roth TL, Oehler DA, Fan J, Rekulapally S. Genomic analysis of demographic history and ecological niche modeling in the endangered sumatran rhinoceros Dicerorhinus sumatrensis. Curr Biol. 2018;28(1):70-76.e74.

    Article  CAS  PubMed  Google Scholar 

  26. Hewitt G. The genetic legacy of the Quaternary ice ages. Nature. 2000;405(6789):907.

    Article  CAS  PubMed  Google Scholar 

  27. Hewitt G. Genetic consequences of climatic oscillations in the Quaternary. Philos Trans R Soc B Biol Sci. 2004;359(1442):183–95.

    Article  CAS  Google Scholar 

  28. Robinson MM, Dowsett HJ, Chandler MA. Pliocene role in assessing future climate impacts. EOS Trans Am Geophys Union. 2008;89(49):501–2.

    Article  Google Scholar 

  29. Comins NF, Kaufmann WJ. Discovering the universe. Macmillan; 2011.

  30. Huang W, Ji H. The first discovery of the triphalanga fauna in Tibet and its significance to the uplift of the plateau. Chin Sci Bull. 1979;19:885–8.

    Google Scholar 

  31. Li J, Fang X. Uplift of the Tibetan Plateau and environmental changes. Chin Sci Bull. 1999;44(23):2117–24.

    Article  Google Scholar 

  32. Zheng H, Powell CM, An Z, Zhou J, Dong G. Pliocene uplift of the northern Tibetan Plateau. Geology. 2000;28(8):715–8.

    Article  Google Scholar 

  33. Mulch A, Chamberlain CP. Earth science: the rise and growth of Tibet. Nature. 2006;439(7077):670.

    Article  CAS  PubMed  Google Scholar 

  34. Wang C, Zhao X, Liu Z, Lippert PC, Graham SA, Coe RS, Yi H, Zhu L, Liu S, Li Y. Constraints on the early uplift history of the Tibetan Plateau. Proc Natl Acad Sci. 2008;105(13):4987–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Xiao X, Li T. Tectonic evolution and uplift of the Qinghai-Tibet Pateau. In: Origin and history of the earth. CRC Press. 2018; pp. 47–59.

  36. Ding D, Liu G, Hou L, Gui W, Chen B, Kang L. Genetic variation in PTPN1 contributes to metabolic adaptation to high-altitude hypoxia in Tibetan migratory locusts. Nat Commun. 2018;9(1):1–12.

    Article  CAS  Google Scholar 

  37. An Z. The history and variability of the East Asian paleomonsoon climate. Quatern Sci Rev. 2000;19(1–5):171–87.

    Article  Google Scholar 

  38. Sun Y, An Z. Late Pliocene-Pleistocene changes in mass accumulation rates of eolian deposits on the central Chinese Loess Plateau. J Geophys Res Atmos. 2005.

    Article  Google Scholar 

  39. Li J, Zhou S, Zhao Z, Zhang J. The Qingzang movement: the major uplift of the Qinghai-Tibetan Plateau. Sci China Earth Sci. 2015;58(11):2113–22.

    Article  Google Scholar 

  40. Liu D. Loess and the environment. China Ocean Press; 1985.

  41. Liu D. Pedosedimentary events in loess of China and Quaternary climatic cycles; 1996.

  42. Ding Z, Sun J, Liu D. Stepwise advance of the Mu Us desert since late pliocene: evidence from a red clay-loess record. Chin Sci Bull. 1999;44(13):1211.

    Article  Google Scholar 

  43. Sun YH, Swenson JE, Fang Y, Klaus S, Scherzinger W. Population ecology of the Chinese grouse, Bonasa sewerzowi, in a fragmented landscape. Biol Conserv. 2003, 110(2):0–184.

  44. Johnsgard PA. The grouse of the world; 1983.

  45. Bergmann H, Klaus S, Müller F, Scherzinger W, Swenson J, Wiesner J. Die Haselhühner, Bonasa bonasia und B. sewerzowi. Die Neue Brehm-Bücherei. Westarp Wissenschaften, Magdeburg, Germany (in German). 1996.

  46. Liu N. Studies on the Southward Migration of Severtzov's Hazel Grouse and it's Incontinuous Distribution with the other Species of Hazel Grouse. J Lanzhou Univ; 1994.

  47. Baba Y, Fujimaki Y, Klaus S, Butorina O, Drovetskii S, Koike H. Molecular population phylogeny of the hazel grouse Bonasa bonasia in East Asia inferred from mitochondrial control-region sequences. Wildl Biol. 2002;8(4):251–60.

    Article  Google Scholar 

  48. Schlee JS. Our changing continent. Washington: Government Printing Office; 1985.

  49. Denton GH, Anderson RF, Toggweiler J, Edwards R, Schaefer J, Putnam A. The last glacial termination. Science. 2010;328(5986):1652–6.

    Article  CAS  PubMed  Google Scholar 

  50. Klaus S, Scherzinger W, Sun Y, Swenson J, Wang J, Fang Y. Das Chinahaselhuhn Tetrastes sewerzowi Akrobat im Weidengebüsch. Limicola. 2009;23:1–57.

    Google Scholar 

  51. Klaus S, Sun Y, Fang Y, Scherzinger W. Autumn territoriality of Chinese Grouse Bonasa sewerzowi at Lianhuashan Natural Reserve, Gansu, China. Inter J Galliform Conserv. 2009;1:44–8.

    Google Scholar 

  52. Lyu N, Sun Y-H. Predicting threat of climate change to the Chinese grouse on the Qinghai—Tibet plateau. Wildl Biol. 2014;20(2):73–82.

    Article  Google Scholar 

  53. The IUCN red list of threatened species.

  54. Wilson RJ, Gutierrez D, Gutierrez J, Monserrat VJ. An elevational shift in butterfly species richness and composition accompanying recent climate change. Glob Change Biol. 2007;13(9):1873–87.

    Article  Google Scholar 

  55. Sekercioglu CH, Schneider SH, Fay JP, Loarie SR. Climate change, elevational range shifts, and bird extinctions. Conserv Biol. 2008;22(1):140–50.

    Article  PubMed  Google Scholar 

  56. Sun Y-H. Distribution and status of the Chinese grouse Bonasa sewerzowi. Wildl Biol. 2000;6(4):271–5.

    Article  CAS  Google Scholar 

  57. Liu N. Studies of species diversity of gallinaceans in Gansu. Zool Res. 1993;14(3):233–9.

    Google Scholar 

  58. Lowe JJ, Walker MJ. Reconstructing quaternary environments. Routledge; 2014.

  59. Zhan X, Zheng Y, Wei F, Bruford MW, Jia C. Molecular evidence for Pleistocene refugia at the eastern edge of the Tibetan Plateau. Mol Ecol. 2011;20(14):3014–26.

    Article  PubMed  Google Scholar 

  60. Beatty GE, Provan J. Refugial persistence and postglacial recolonization of North America by the cold-tolerant herbaceous plant Orthilia secunda. Mol Ecol. 2010;19(22):5009–21.

    Article  PubMed  Google Scholar 

  61. Kajtoch Ł, Żmihorski M, Bonczar Z. Hazel Grouse occurrence in fragmented forests: habitat quantity and configuration is more important than quality. Eur J Forest Res. 2012;131(6):1783–95.

    Article  Google Scholar 

  62. Rózsa J, Strand TM, Montadert M, Kozma R, Höglund J. Effects of a range expansion on adaptive and neutral genetic diversity in dispersal limited Hazel grouse (Bonasa bonasia) in the French Alps. Conserv Genet. 2016;17(2):401–12.

    Article  Google Scholar 

  63. Weir JT, Schluter D. Ice sheets promote speciation in boreal birds. Proc R Soc London B Biol Sci. 2004;271(1551):1881–7.

    Article  Google Scholar 

  64. Ingolfsson O, Moller P, Lubinski D, Forman S. Severnaya Zemlya, Arctic Russia: a Nucleation Area for Kara Sea Ice Sheets during the middle to late quaternary. In: AGU Fall Meeting Abstracts; 2006.

  65. Saarnisto M, Lunkka JP. Climate variability during the last interglacial-glacial cycle in NW Eurasia. In: Past Climate Variability through Europe and Africa. Springer; 2004: 443–464.

  66. Mygatt E. World’s forests continue to shrink. Washington: Earth Policy Institute; 2006.

    Google Scholar 

  67. Um T, Kim K, Im Y, Lee H. Forest and human health. Sangji Univ. 2009;12:16.

    Google Scholar 

  68. Perlin J. A forest journey: the story of wood and civilization. The Countryman Press; 2005.

  69. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. 1000 genome project data processing subgroup. The sequence alignment/map format and samtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Vilella AJ, Severin J, Ureta-Vidal A, Heng L, Durbin R, Birney E. EnsemblCompara GeneTrees: complete, duplication-aware phylogenetic trees in vertebrates. Genome Res. 2009;19(2):327–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24(8):1586–91.

    Article  CAS  PubMed  Google Scholar 

  75. Schiffels S, Durbin R. Inferring human population size and separation history from multiple genome sequences. Nat Genet. 2014;46(8):919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Kozma R, Melsted P, Magnússon KP, Höglund J. Looking into the past—the reaction of three grouse species to climate change over the last million years using whole genome sequences. Mol Ecol. 2016;25(2):570–80.

    Article  PubMed  Google Scholar 

  77. Kozma R. Inferring demographic history and speciation of grouse using whole genome sequences. Acta Universitatis Upsaliensis; 2016.

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We would like to thank Annoroad Gene Technology in Beijing for performing the whole genome sequencing. This project is funded by National Natural Science Foundation of China (NSFC: 31520103903 to SYH, JH).


Open access funding provided by Uppsala University. Funding for this project was provided by National Natural Science Foundation of China (NSFC: 31520103903 to SYH, JH). Field work and de novo sequences were supported by this funding. The funding body played no role in the design of the study and collection, analysis and interpretation pf data and in writing the manuscript.

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Authors and Affiliations



KS, JH and YHS designed and managed the project. KS, BG and PH performed the analyses. KS, YF and YJ collected the samples. KS, JS, JH and YHS wrote the paper. JH, YHS, JS, BG, PH, YF and YJ revised the paper. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Kai Song, Yue-Hua Sun or Jacob Höglund.

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Ethics approval and consent to participate

This study was approved by the Animal Ethics Committee of the Institute of Zoology, Chinese Academy of Sciences (IOZ20150069). The blood samples were collected from wild Chinese Grouse, which were released back in to the wild. The procedure of blood collection was in strict accordance with the Animal Ethics Procedures and Guidelines of the People’s Republic of China.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Table. S1.

Sample information and the statistic of whole genome quality control. Fig. S1. Estimated ancestral relationships of Grouse based on Maximum Likelihood using RAxML. Fig. S2. Neighbor-joining tree constructed from Nei’s standard genetic distances of the whole genome sequences of Chinese and Hazel grouse. Numbers at the nodes indicate bootstrap support. Substitution rate is indicated below the figure.

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Song, K., Gao, B., Halvarsson, P. et al. Demographic history and divergence of sibling grouse species inferred from whole genome sequencing reveal past effects of climate change. BMC Ecol Evo 21, 194 (2021).

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