The Eastern Eurasian Steppe was home to historic empires of nomadic pastoralists, including the Xiongnu and the Mongols. However, little is known about the region’s population history. Here, we reveal its dynamic genetic history by analyzing new genome-wide data for 214 ancient individuals spanning 6,000 years. We identify a pastoralist expansion into Mongolia ca. 3000 BCE, and by the Late Bronze Age, Mongolian populations were biogeographically structured into three distinct groups, all practicing dairy pastoralism regardless of ancestry. The Xiongnu emerged from the mixing of these populations and those from surrounding regions. By comparison, the Mongols exhibit much higher eastern Eurasian ancestry, resembling present-day Mongolic-speaking populations. Our results illuminate the complex interplay between genetic, sociopolitical, and cultural changes on the Eastern Steppe.  •Genome-wide analysis of 214 ancient individuals from Mongolia and the Baikal region•Three genetically distinct dairy pastoralist groups in Late Bronze Age Mongolia•Xiongnu nomadic empire formed through mixing of distinct local and distant groups•No selection on the lactase persistence alleles despite 5,000 years of dairy culture Ancient DNA from over 200 individuals inhabiting the Eastern Eurasian Steppe during the Bronze Age provides insights into the population history of the Mongols and Xiongnu. Recent paleogenomic studies have revealed a dynamic population history on the Eurasian Steppe, with continental-scale migration events on the Western Steppe coinciding with Bronze Age transformations of Europe, the Near East, and the Caucasus . HoweverDuring the Bronze Age, the multi-phased introduction of pastoralism drastically changed lifeways and subsistence on the Eastern Steppe . A recenFrom the late first millennium BCE onward, a series of hierarchical and centrally organized empires arose on the Eastern Steppe, notably the Xiongnu (209 BCE–98 CE), Türkic (552–742 CE), Uyghur (744–840 CE), and Khitan (916–1125 CE) empires. The Xiongnu empire was the first such polity in the steppe, whose drastic expansions into northern China, southern Siberia, and deep into Central Asia had a profound impact on the demographics and geopolitics of Eurasia. The Mongol empire, emerging in the thirteenth century CE, was the last and most expansive of these regimes, eventually controlling vast territories and trade routes stretching from China to the Mediterranean. However, due to a lack of large-scale genetic studies, the origins and relationships of the people who formed these states, including both the ruling elites and local commoners, remain obscure.To clarify the population dynamics on the Eastern Steppe since prehistory, we generated and analyzed genome-wide genetic datasets for 214 individuals from 85 Mongolian and 3 Russian sites spanning approximately 6,000 years of time (ca. 4600 BCE to 1400 CE) , and S3AIn this study, we analyzed six pre-Bronze Age hunter-gatherer individuals from three sites dating to the fifth and fourth millennia BCE: one from eastern Mongolia , one from central Mongolia , and four from the eastern Baikal region (“Fofonovo_EN”). By comparing these genomes to previously published ancient and modern data across Eurasia C, we fouPastoralism in Mongolia is often assumed to have been introduced by the eastward expansion of Western Steppe cultures  via either the Upper Yenisei and Sayan mountain region to the northwest of Mongolia or through the Altai mountains in the west . AlthougThe succeeding EBA Chemurchek culture (2750–1900 BCE), a ruminant dairying society  whose moAlthough based on a small number of genomes, we find that neither the Afanasievo nor the Chemurchek left enduring genetic traces into the subsequent MLBA. This is strikingly different than in Europe, where migrating EBA steppe herders had a transformative and lasting genetic impact on local populations . In the Previously, we reported a shared genetic profile among EBA western Baikal hunter-gatherers  and Late Bronze Age (LBA) pastoralists in northern Mongolia (Khövsgöl_LBA) . This geairag) production (During the MLBA (1900–900 BCE), as grasslands expanded in response to climate change, new pastoralist cultures expanded out of inner-montane regions and across the Eastern Steppe . This peoduction , and a doduction B. Overaloduction , with thoduction C.This is the first appearance on the Eastern Steppe of a Sintashta-like ancestry (frequently referred to as “steppe_MLBA” in previous studies), which is distinct from prior Western ancestries present in the Afanasievo and Chemurchek populations and instead shows a close affinity to European Corded-Ware populations and later Andronovo-associated groups, such as the Sintashta . In KhovThree MLBA individuals in our dataset present genetic profiles that cannot be fully explained by the Altai_MLBA cline. These three, two Altai individuals (UAA001 and KHI001) and UUS001 from Khövsgöl province, are better modeled with a small contribution from Gonur1_BA as a third ancestry source C. Taken The populations making up the heterogeneous Altai_MLBA cline left descendants in the Altai-Sayan region, who we later identify at the Sagly/Uyuk site of Chandman Mountain  in northwestern Mongolia during the Early Iron Age (EIA). Nine Chandman_IA individuals form a tight cluster on PCA at the end of the previous Altai_MLBA cline away from Khövsgöl_LBA cluster . During We find that EIA Scythian populations systematically deviate from the earlier Altai_MLBA cline, requiring a third ancestral component . The appIn contrast to the MLBA and EIA cultures of the Altai and northern Mongolia, different burial traditions are found in the eastern and southern regions of Mongolia , notablyArising from the prehistoric populations of the Eastern Steppe, large-scale polities began to develop during the late first millennium BCE. The Xiongnu was the first historically documented empire founded by pastoralists, and its establishment is considered a watershed event in the sociopolitical history of the Eastern Steppe . The XioWe observe two distinct demographic processes that contributed to the formation of the early Xiongnu. First, half of the early individuals (n = 6) form a genetic cluster (earlyXiongnu_west) resembling that of Chandman_IA of the preceding Sagly/Uyuk culture from the Altai-Sayan region . They deAmong late Xiongnu individuals, we find even higher genetic heterogeneity , and theAdmixture modeling confirms the presence of a Sarmatian-related gene pool among the late Xiongnu: three individuals  are indistinguishable from Sarmatian, two individuals  are admixed between Sarmatian and BMAC, three individuals  are admixed between Sarmatian and Ulaanzuukh_SlabGrave, and three individuals  require Sarmatian, BMAC, and Ulaanzuukh_SlabGrave G. In addAfter the collapse of the Xiongnu empire ca. 100 CE, a succession of nomadic pastoralist regimes rose and fell over the next several centuries across the politically fragmented Eastern Steppe: Xianbei (ca. 100–250 CE), Rouran (ca. 300–550 CE), Türkic 552–742 CE), and Uyghur (744–840 CE). Although our sample representation for the Early Medieval period is uneven, consisting of 1 unclassified individual dating to the Xianbei or Rouran period (TUK001), 8 individuals from Türkic mortuary contexts, and 13 individuals from Uyghur cemeteries, it is clear that these individuals have genetic profiles that differ from the preceding Xiongnu period, suggesting new sources of gene flow into Mongolia at this time that displace them along PC3 . Individ2 CE, andThe high genetic heterogeneity of the Early Medieval period is vividly exemplified by 12 individuals from the Uyghur period cemetery of Olon Dov OLN;  in the vAfter the fall of the Uyghur empire in the mid-ninth century, the Khitans of northeast China established the powerful Liao Dynasty in 916 CE. The Khitans controlled large areas of the Eastern Steppe and are recorded to have relocated people within their conquered territories , but fewAt its greatest extent, the Mongol empire (1206–1368 CE) spanned nearly two-thirds of the Eurasian continent. It was the world’s largest contiguous land empire, and the cosmopolitan entity comprised diverse populations that flowed into the steppe heartland. We analyzed 62 Mongol-era individuals whose burials are consistent with those of low-level, local elites. No royal or regional elite burials were included, and neither were individuals from the cosmopolitan capital of Karakorum. Although we find that Mongol-era individuals were diverse, they exhibit a much lower genetic heterogeneity than the Xiongnu-era individuals , and theSince the fall of the Mongol empire in 1368 CE, the genetic profile of the Mongolian populations has not substantially changed. The genetic structure established during the Mongol empire continues to characterize present-day Mongolic-speaking populations living in both Mongolia and Russia. We examined the genetic cladality between the historic Mongols and seven present-day Mongolic-speaking groups  using an individual-based qpWave analysis. Within the resolution of current data, 34 of 61 historic Mongols are genetically cladal with at least one modern Mongolic-speaking population B. The MoLCT/MCM6), dental morphology (EDAR), pigmentation , and alcohol metabolism (ADH1B)  per day during the summer months  A. First, (ADH1B) , the MLB (ADH1B) A. This ir months , resultiei Basin , and accproducts , which hproducts . How Monium spp. .Figure 5EDAR (ectodysplasin A receptor) and rs1426654 in SLC24A5 (solute carrier family 24 member 5) are well-known targets of positive selection in East Asians and western Eurasians, respectively  and rs1800414 in OCA2 , were absent or in extremely low frequency during the MLBA and EIA, when the eastern Eurasian ancestry was primarily ANA-related, but increased in frequency over time as the proportion of East Asian ancestry increased through interactions with imperial China and other groups  at Jargalantyn Am, as well as a mother-son pair (IMA002 and IMA005) at Il’movaya Pad, a brother-sister pair (TMI001 and BUR003) at Tamiryn Ulaan Khoshuu, and a brother-brother pair (SKT002 and SKT006) at Salkhityn Am D. Of theThe population history of the Eastern Steppe is one marked by the repeated mixing of diverse eastern and western Eurasian gene pools. However, rather than simple waves of migration, demographic events on the Eastern Steppe have been complex and variable. Generating more than 200 genome-wide ancient datasets, we have presented the first genetic evidence of this dynamic population history, from ca. 4600 BCE through the end of the Mongol empire. We found that the Eastern Steppe was populated by hunter-gatherers of ANA and ANE ancestry during the mid-Holocene and then shifted to a dairy pastoralist economy during the Bronze Age. Migrating Yamnaya/Afanasievo steppe herders, equipped with carts and domestic livestock , appear During the MLBA, we observed the formation of a tripartite genetic structure on the Eastern Steppe, characterized by the continuation of pre-Bronze Age ANA ancestry in the east and a cline of genetic variation between pre-Bronze Age ANA-ANE ancestry in the north and increasing proportions of a new Sintashta-related WSH ancestry in the west. The Sintashta, a western forest steppe culture with genetic links to the European Corded Ware cultures , were maGenetic data for the subsequent Early Medieval period are relatively sparse and uneven, and few Xianbei or Rouran sites have yet been identified during the 400-year gap between the Xiongnu and Türkic periods. We observed high genetic heterogeneity and diversity during the Türkic and Uyghur periods, and following the collapse of the Uyghur empire, we documented a final major genetic shift during the late medieval period toward greater eastern Eurasian ancestry, which is consistent with historically documented expansions of Tungusic- (Jurchen) and Mongolic- (Khitan and Mongol) speaking groups from the northeast into the Eastern Steppe . We alsoHaving documented key periods of genetic shifts in the Eastern steppe, future work may be able to explore whether these shifts are also linked to cultural and technological innovations and how these innovations may have influenced the political landscape. Integrating these findings with research on changes in horse technology and herding practices, as well as shifts in livestock traits and breeds, may prove particularly illuminating. This study represents the first large-scale paleogenomic investigation of the Eastern Eurasian Steppe, and it sheds light on the remarkably complex and dynamic genetic diversity of the region. Despite this progress, there is still a great need for further genetic research in central and eastern Eurasia, and particularly in northeastern China, the Tarim Basin, and the eastern Kazakh steppe, in order to fully reveal the population history of the Eurasian Steppe and its pivotal role in world prehistory.warinner@fas.harvard.edu).Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Christina Warinner  pre-Bronze Age, prior to 3500 BCE; (2) Early Bronze Age, 3500-1900 BCE; (3) Middle/Late Bronze Age, 1900-900 BCE; (4) Early Iron Age, 900-300 BCE; (5) Xiongnu, 200 BCE to 100 CE; (6) Early Medieval, 100-850 CE, and (7) Late Medieval, 850-1650 CE. A brief summary of each period, as well as details for the sites included in this study, are provided below, in https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). The second, dating to ca. 3700 BCE, is from Erdenemandal (ERM) in the Arkhangai province of north Mongolia (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). It lacked stone construction features and consisted of a simple crouched pit burial beneath a shallow earthen mound. The burial was recovered at a depth of 2.3 m and the grave mound appears to be part of a larger cemetery.The early archaeological record of Mongolia is poorly understood, particularly with respect to human remains and burials. While occasional finds provide direct evidence of anatomically modern humans in Mongolia as far back as the Early Upper Paleolithic , only a In addition to these two pre-Bronze burials from Mongolia, we also analyzed four pre-Bronze individuals from the site of Fofonovo (FNO) near Lake Baikal in Buryatia, Russia . All werAlthough pre-Bronze Age material from Mongolia is sparse, recent excavations in neighboring regions provide important context. In southern Russia, excavations by the Baikal Archaeological Project (BAP) at three sites  have enabled characterization of the Lake Baikal Neolithic Kitoi (5200-4200 BCE) and Isakovo (4000-3000 BCE) mortuary traditions, including genome sequencing of 14 of these hunter-gatherers  Baikal_. The genTwo major cultural phenomena associated with monumental mortuary architecture have been described in Mongolia during the Early Bronze Age (EBA): Afanasievo and Chemurchek. Both exhibit features linking them to ruminant pastoralism and to cultures further west.Afanasievo (3150-2750 BCE). Beginning ca. 3150 BCE and persisting until ca. 2750 BCE (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo).2750 BCE , althoug2750 BCE , which cIn addition to domestic animal remains, Afanasievo burial mounds contain egg-shaped pottery vessels, and sometimes include metal artifacts  and apparent deconstructed cart objects . Recent We analyzed individuals from one Afanasievo site in this study: Shatar Chuluu (SHT). Located in Byankhongor province on the south slope of the Khangai mountains, Shatatar Chuulu is the easternmost known Afanasievo cemetery in Eurasia. Three of the site’s burial mounds have been excavated, and each consisted of a flat platform of round stones bounded by large boulders. The burials were arranged in pits beneath the mounds, and the bodies were laid out in a supine position with flexed knees and heads facing to the west. Despite the fact that few burial goods were found, the overall architectural design of the mounds combined with isolated fragments of typical Afanasievo vessels make it possible to attribute these mounds to the Afanasievo archaeological culture.Chemurchek (2750-1900 BCE). The Chemurchek archaeological culture , spans the period between 2750 BCE-1900 BCE (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). Similar to Afanasievo burials, individuals found in Chemurchek tombs are laid out in a supine position with flexed legs. Adjacent to many Chemurchek burial features along the eastern side are anthropomorphic standing stones, sometimes depicted holding a shepherd’s crook (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). Inside the burials, artifacts such as stone bowls, bone tools, ceramics, and sometimes metal jewelry, have been recovered. Occasionally, non-funerary ritual structures, such as fences containing earthen pits with charcoal and animal remains or large stone fences depicting petroglyphs, are also attributed to this culture .Comparative genomic data are available for several contemporaneous sites in neighboring regions, including: (1) Botai, a horse hunter-herder site dating to ca. 3500 BCE in northern Kazakhstan ; (2) mulThe Middle/Late Bronze Age (MLBA) in Mongolia is characterized by the sudden and widespread appearance of monumental mortuary architecture across Mongolia. Primarily taking the form of stone mounds, but also including stone stelae and other features, these Middle and Late Bronze Age structures remain among the most conspicuous features on the landscape even today. Middle and Late Bronze Age burial mound typology is complex and there is scholarly debate and disagreement on how to precisely define and delineate different mortuary types. In this study, we focused on several main burial forms: Mönkhkhairkhan, Baitag, Deer Stone-Khirigsuur Complex (DSKC), Ulaanzuukh, and Tevsh (Shape). We provide a general overview of these burial types, but acknowledge that not all scholars will agree with all details.Mönkhkhairkhan (1850-1350 BCE). Dating to after the Chemurchek period, ca. 1850-1350 cal. BCE (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). Overlaying the burials are external stone structures consisting of flat round or rectangular platforms. Such barrows are typically 3-5 m in diameter, but occasionally reach up to 40 m in diameter. Ritual structures may include stone circles and stelae. No pottery or animal bones have been reported from within Mönkhkhairkhan burials, and little is known of its economy. Burial goods include tin bronze knives and awls, tin bronze two-trumpet shaped rings, bone spoons, bone arrowheads, and ornaments made of bone, shell, and stone . Restricted to southwestern Mongolia, Baitag burials consist of non-mounded, small stone rings constructed from a single layer of small flat stone slabs, as seen for example at the site of Uyench  (Ta18jEo) . A centrTa18jEo) . In thisDeer Stone-Khirigsuur Complex (DSKC) (1350-900 BCE). This culture comprises three different monumental features - khirigsuurs, deer stones, and Sagsai-style graves - and is tightly associated with the emergence of horsemanship in the Mongolian Steppe during the late second millennium BCE. In general, DSKC sites are concentrated in the western, northern, and central parts of Mongolia, with only a small number of sites further east . In the Mongolian Altai, some khirigsuurs display stone lines between the central cairn and outer fence, producing a shape that resembles a spoked wheel. Although their exclusive function as burials is a subject of contention , smaller stone mounds containing the head, jaw, neck, and hooves of individual horses are found surrounding the eastern perimeter of the monument (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). These horse mounds can range in number from a handful into the hundreds or thousands. Osteological study of DSKC horses reveal their use in transport and likely riding, as well as their sophisticated management as herd animals .Sagsai-style graves  are oftekhirigsuurs to between ca. 1350-900 cal BCE, deer stones to ca. 1150-750 cal. BCE, and places the emergence of DSKC horse ritual at ca. 1200 cal BCE  (1450-1150 BCE). Beginning in the mid-second millennium BCE, a number of different burial traditions emerged in the southern and southeastern regions of Mongolia. United by a common prone or face-down burial position, these groups were once considered part of the Slab Grave culture, but are now either classified separately as discrete burial types Ulaanzuukh and Tevsh/Shape (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). Tevsh burials, also called Shape burials, are found throughout southern Mongolia and central Inner Mongolia and are similar to Ulaanzuukh burials except that they are hourglass-shaped (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). The walls of Tevsh/Shape burials are typically made of layered stone (masonry), and sometimes with a single edge ringed with upright slabs. Other burial styles in the region, which may represent variant types, include D-shaped and stirrup-shaped burial structures.sh/Shape  or are ssh/Shape . Ulaanzush/Shape . The sitRadiocarbon modeling suggests that Ulaanzuukh features date to ca. 1450-1150 BCE, while shape burials could both predate and postdate this mark – although very few have been reliably dated . BurialsUnclassified. In addition to these main types, we also analyzed individuals from six Late Bronze Age sites containing burials with uncertain or unclassified cultural affiliations: Biluutiin Am (BIL), Khoit Tsenkher (KHI/KHO), Shar Gobi 3 (SBG), Tsaidam Bag (TSB/TSI), Uliastai River lower terrace (ULI), and Uliastai Zastav II (ULZ). For more information on these burials, see Comparative genomic data are available for several contemporaneous archaeological sites in neighboring regions, including: (1) four Okunevo sites , dating to 2200-2600 BCE ; (2) fivThe Early Iron Age cultures of Inner Asia arose during a time of new technological advancements, including the development of composite bows and the beginnings of iron metallurgy used for items like arrows and horse-riding equipment . These cSlab Grave (1000-300 BCE). Beginning around 1000 BCE, a new burial style known as Slab Grave began appearing in eastern Mongolia. Slab graves are so called because of the large stone slabs used to mark the surface of the burial and to contain the rectangular burial space  wherein single individuals are interred (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). The burial pits are quite shallow, and human remains are rarely found complete or in good preservation. Over time, the Slab Grave culture expands northward into eastern Baikal and westward into central Mongolia, where it intrudes into former DSKC territory. Some slab graves tear apart the stone structures of khirigsuurs to construct the graves, and some even reuse deer stones for standing corner stones or laid-down slabs within the burial pit . This Early Iron Age culture centered in the Upper Yenisei River area, in modern-day Tuva, with some extensions into northwestern Mongolia (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). Graves were marked by a round pile of stones and are often found in cemeteries of one to two dozen graves. Beneath the stone mounds are large log chambers containing several individuals  all laid in partially flexed positions on their sides. Portions of sheep are also often placed in the graves. The Sagly/Uyuk log chambers resemble similar log architecture constructed by the contemporaneous Pazyryk culture in the Russian Altai and surrounding areas, and both the Sagly/Uyuk and Pazryk have been associated with the broader Saka culture . This culture is known mainly for its type site of Pazyryk, whose large tombs contain numerous exotic imports, including silks from China and textiles from Achaemenid Persia (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). No new Pazyryk individuals were included in this study; however, they are important to consider because the northern Altai practice of whole horse burials later appears in scattered central Mongolia cemeteries of the subsequent Xiongnu period. Genome-wide data from Pazyryk individuals have been previously reported from site of Berel in Altai region of Kazakhstan  the early Sarmatian site Pokrovka in southwestern Russia, dating to ca. 500-100 BCE , a ScythDuring the late first millennium BCE, a radically new multi-regional political entity formed in Mongolia, known as the Xiongnu empire. The Xiongnu empire is attested not only by historical records but also by ample archaeological remains throughout Inner Asia . For rouWhile ring grave structures are found throughout the entire Xiongnu era, prestige accoutrements  changed during the course of the empire. According to these changes, we can discern a general division between Early (200-50 BCE) and Late (50 BCE - 100 CE) Xiongnu periods . OverallEarly Xiongnu (200-50 BCE). Prestige items during the Early Xiongnu period are dominated by large bronze belt pieces; however, burial customs within graves of the Early period varied to a great degree between regions. One example of this occurs at Salkhityn Am cemetery, where rituals of ring graves show a high degree of variation, even including offerings of whole horses that are more typical of Altai elites such as those in Pazyryk graves (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). We analyzed individuals from three Early Xiongnu sites in this study: Astyn Gol (AST), Buural Uul (BAU/BRL/BUU), and Salkhityn Am (SKT).k graves  (https:/Late Xiongnu (50 BCE - 100 CE). Prestige items in the Late Xiongnu period shift to more iron items, often covered with gold foil or even inlaid with precious stones, and increasingly focused on long-distance exotic materials. At the same time, burial customs in ring graves throughout the empire become more regularized. Most elites were buried in wooden coffins in shaft pits with livestock portions and ceramic vessels set beside the coffin (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). During the Late period, the high ruling Xiongnu elites adopted a radically new form of burial structure. These square tombs were marked on the surface by rectangular stone structures with trapezoidal ‘ramp’ entryways, their burial pits were extremely deep, and wooden coffins were decorated and nested within larger wooden chambers (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). We analyzed individuals from 26 Late Xiongnu sites in this study: Atsyn Am (ATS), Baruun Mukhdagiin Am (BAM), Baruun Khovdiin Am (BRU), Burkhan Tolgoi (BTO), Chandman Mountain (CHN), Delgerkhaan Uul (DEL), Khanan Uul (DOL), Duulga Uul (DUU); Emeel Tolgoi (EME), Khudgiin Am (HUD), Ikh Tokhoirol (IKT), Il’movaya Pad (IMA), Jargalantyn Am , Tarvagatain Am , Naimaa Tolgoi (NAI), Sant Uul (SAN), Solbi Uul (SOL), Songino Khairkhan (SON), Takhityn Khotgor (TAK), Tavan Tolgoi (TAV), Tevsh Mountain (TEV), Ulaanzuukh (ULN), Ovgont (UVG), Yuroo II (YUR), Tamiryn Ulaan Khoshuu , and Uguumur Uul (UGU).Comparative genomic data are available for a few contemporaneous sites in neighboring regions, including: (1) two early Xiongnu individuals from Khövsgöl (Hovsgol) province dating to 50-350 BCE ; (2) a lAfter the fall of the Xiongnu, Xianbei groups from northeast China pushed into Mongolia, although historical and archaeological evidence for the establishment of large and long-lasting Xianbei polities appears only in northern China, not in Mongolia . One indThe most prominent political entities in the Early Medieval era are the Türk and Uyghur empires, the latter being an immediate dynastic takeover from the former. Numerous burials of the Türk era have been unearthed in Mongolia. By contrast, far fewer Uyghur burials have been identified and excavated to date.Türk (550-750 CE). Göktürkic tribes of the Altai Mountains established a political structure across Eurasia beginning in 552 CE, with an empire that ruled over Mongolia from 581-742 CE (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). Other burials were in small wooden coffins without whole horses beside them. We analyzed individuals from 5 Türk sites in this study: Nomgonii Khundii (NOM), Shoroon Bumbagar , Zaan-Khoshuu (ZAA), Uliastai River Lower Terrace (ULI), and Umuumur uul (UGU).1-742 CE . A briefUyghur (750-850 CE). In the mid-eighth century, Uyghur tribes from the Upper Yenesei region overthrew the Türk rulers and immediately established a Mongolia-based empire, taking over the Orkhon valley as their capital and establishing a dynasty from 744-840 CE (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). One individual in this study (OLN006) was found in a monumental tomb. A second, more modest category of Uyghur burials consists of stone structures placed on the surface, either square or round in shape, that contain multiple individuals (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). Dozens of these burials have been documented at Olondov  Alan sites in North Ossetia-Alania and Alan 51 from the Caucasus ; (2) theThis period in Mongolia is dominated mostly by the power struggles of two empires established by the Khitans (907-1125 CE) and the Mongols (1206-1368 CE). Burials from the Khitan era are virtually unknown in Mongolia, whereas numerous graves from the Mongol era have been documented and unearthed. So-called cave burials are known from both periods , but theKhitan (ca. 900-1100 CE). After the collapse of the Uyghur empire in Mongolia in 840 CE, the Khitans of northeast China established the powerful Liao Dynasty in 916 CE. Although based in Manchuria, the Khitans conquered and controlled the steppe of present-day Mongolia through a system of garrisons and long walls, deporting people from other conquered regions, such as northern Korea, to Mongolia (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). These burials are significantly different in form and structure from other Khitan burials in northern China, where the core of the empire was located. At present, no monumental tombs of high Khitan elites have been found in Mongolia.Mongolia . The disMongolia . The manMongol (ca. 1200-1400 CE). The home base of the Mongol tribe was in the forest-steppe zone at the Onon and Kerülen (Kherlen) rivers in northeastern Mongolia. From this core region they successfully conquered the Eurasian steppes and most of their sedentary neighbors in the adjacent regions. Historical records indicate that they transferred a large number of defeated people, war captives and slaves all over their growing empire; they also fostered trade, the exchange of knowledge, techniques, and technicians (https://edmond.mpdl.mpg.de/imeji/collection/2ZJSw35ZTTa18jEo). The pit is normally between 50-150 cm deep, rarely deeper, and very seldom constructed as a niche. Typical Mongol burials contain one person placed in a supine position and sometimes in a wooden coffin, with the head to the north. A very characteristic feature of Mongol burials is the inclusion of a tibia from small livestock, mostly sheep, placed near the head and sometimes in a vessel. There are two ideal burial types concerning grave goods: one equipped with bow, arrow, quiver, horse equipment, and belt with attachments, and a second with scissors, a comb, a mirror, beads and a bogtag – a long hat made out of birch bark, covered with silk and decorated with golden ornament. Graves of these standard types are spread all over Mongolia, and at present no regional differences have been reported and no monumental burials are known , Tsaidam Bag (TSB/TSI), Uushigiin Uver (UUS).Because no comparative genomic data are available for contemporaneous sites, we compared our Late Medieval data to modern Mongolic speaking populations  .A total of 30 new radiocarbon dates were obtained by accelerator mass spectrometry (AMS) of bone and tooth material at the Curt-Engelhorn-Zentrum Archäometrie (CEZA) in Mannheim, Germany (n = 28) and the University of Cologne Centre for Accelerator Mass Spectrometry (CologneAMS) (n = 2). Selection for radiocarbon dating was made for all burials with ambiguous or unusual burial context and for all individuals appearing as genetic outliers for their assigned period. Uncalibrated direct carbon dates were successfully obtained for all bone and tooth samples . An addiOf the 104 total radiocarbon dates analyzed in this study, 25 conflicted with archaeological period designations reported in excavation field notes or previous publications . Four buIn addition to early burials being mistaken for later ones, late burials were also misassigned to earlier periods. For example, three burials originally assigned to the Middle/Late Bronze Age were determined to date to the Early Iron Age (DAR001), Xiongnu (ULN004), and Late Medieval (SHU001) periods, and two Early Iron Age , six Xiongnu , and two Early Medieval  graves were likewise reassigned to later periods following radiocarbon dating. Part of the difficulty in correctly assigning archaeological period to later burials relates to the frequent reuse of earlier graves and cemeteries by populations from later periods. The site reports of several Xiongnu excavations noted burial intrusions, displaced burials, and other indications of burial disturbance and reuse. However, evidence of burial reuse may also be subtle and easily overlooked. As such, we recommend great care in making cultural or temporal assignments at multi-period cemeteries or for any burials showing evidence of disturbance.Sampling was performed on a total of 169 teeth and 75 petrosal bones from fragmented crania originating from 225 individuals C. For 14Genomic DNA extraction and Illumina double-stranded DNA (dsDNA) sequencing library preparation were performed for all samples in a dedicated ancient DNA clean room facility at the MPI-SHH, following published protocols  with slihttps://github.com/stschiff/sequenceTools). For C/T and G/A SNPs, we used end-masked BAM files, and for the others we used the original unmasked BAM files. For the eight ssDNA libraries, we used end-masked BAM files for C/T SNPs, and the original BAM files for the others.Short read sequencing data were processed by an automated workflow using the EAGER v1.92.55 program . SpecifiIn cases where more than one sample was genetically analyzed per individual, we compared the amount of human DNA between samples. For pairs of petrous bone and teeth, human DNA was higher in the petrous bone in 8 of 13 individuals, and higher in the teeth of 5 of 13 individuals B. In addOf the 225 new individuals analyzed, 18 failed to yield sufficient human DNA (< 0.1%) on shotgun screening A and a fTo confirm that our sequence data consist of endogenous genomic DNA from ancient individuals with minimal contamination, we collected multiple data quality statistics. First, we tabulated 5′ C>T and 3′ G>A misincorporation rate A as a fuTen individuals were estimated to have > 5% DNA contamination  or uncertain genetic sex A; these We calculated the genetic sequence coverage on the autosomes and on each sex chromosome in order to obtain the ratio between the sex chromosome coverage and the autosome coverage. For 1240K capture data, we observe females to have an approximately even ratio of X to autosomal coverage (X-ratio of ∼0.8) and a Y-ratio of 0, and males to have approximately half the coverage on X and Y as autosomes (∼0.4). Genetic sex could be determined for a total of 224 individuals, of which 100 were female and 124 were male C.https://github.com/alexhbnr/yhaplo. This version takes into account high missing rate of aDNA data to prevent the program from stopping its root-to-tip haplogroup search prematurely at an internal branch due to missing SNP and therefore assigning a wrong haplogroup. We used “–ancStopThresh 10” following the developer’s recommendation. Haplogroup assignments are shown in We called mitochondrial consensus sequence from the Schmutzi output using the log2fasta program in the Schmutzi package, with quality threshold of 10. We then assigned each consensus sequence into a haplogroup C using tTo evaluate the relatedness within our dataset, we calculated pairwise mismatch rate of haploid genotypes on automosomes across all individuals. The pairwise mismatch rate for each pair of individuals, is defined as the number of sites where two individuals have different alleles sampled divided by the total number of sites that both individuals have data. The pairwise mismatch rate between unrelated individuals is set as the baseline and the coefficient of relationship is inversely linear to the baseline pairwise mismatch rate. More detailed description can be found in the A total of 15 first or second degree genetic relationships were observed across the dataset D, of whiTo analyze our dataset in the context of known ancient and modern genetic diversity, we merged it with previous published modern genomic data from i) 225 worldwide populations genotyped on the Human Origins array , ii) 300f3 and f4 statistics using qp3Pop v435 and qpDstat v755 in the admixtools v5.1 package  on the merged dataset with the Human Origins data using the smartpca v16000 in the Eigensoft v7.2.1 package . Modern  package . We addef4 statistics, i.e., one can find a linear combination of the sources that is symmetrically related to the target in terms of their relationship to all outgroups in the analysis. qpAdm optimizes the admixture coefficients to match the observed f4 statistics matrix, and reports a p-value for the null hypothesis that the target derives their ancestry from the chosen sources that are differently related to the outgroups . The chosen outgroups in qpAdm needs to be differentially related to the sources such that a certain major ancestry is “anchored” in the test, which is rather heuristic. We used qpWave to test the resolution of a set of outgroups for distinguishing major ancestries among Eurasians, as well as the genetic cladility between populations given a set of outgroups. We used a set of eight outgroup populations in our study: Central African hunter-gatherers Mbuti.DG (n = 5), indigenous Andamanese islanders Onge.DG (n = 2), Taiwanese Aborigines Ami.DG (n = 2), Native Americans Mixe.DG (n = 3), early Holocene Levantine hunter-gatherers Natufian (n = 6)  , early N (n = 6) , early N (n = 6) , and a P (n = 6) .A and PX divided by their standard errors , skin pigmentation , alcohol metabolism (ADH1B), and epithelial phenotypes including shovel-shaped incisor (EDAR) (We examined 49 SNPs in 17 genes E known tr (EDAR) .f3 and symmetric-f4 statistics to (1) quantify genetic similarity between individuals/groups falling together on PCA and (2) explore populations whose ancestry through admixture may have contributed to the differences observed between pairs of groups. Third, we identified representative ancient populations to serve as proxies for five distinct ancestries that we then further investigate , centralMongolia_preBA(1), and Fofonovo_EN(4)⋅ Published genetic groups: DevilsCave_N(6), and Baikal_EN(9)Our dataset adds three Ancient Northeast Asian (ANA)-related genetic groups before the start of the Bronze Age in eastern Eurasia. During this period, we observe the wide distribution of this ANA ancestry from Lake Baikal to the Russian Far East, spanning more than 2,000 km. As Baikal_EN has been modeled to have ∼10% Ancient North Eurasian (ANE) ancestry, we also investigated the possible genetic contribution from ANE in our pre-Bronze Age Mongolian and Baikal groups using Botai, AG3, MA1 and West_Siberia_N separately as ancestry proxies. We find ANE-related ancestry appears in centralMongolia_preBA and Fofonovo_EN only to a minor extent; ANE ancestry is not present in eastMongolia_preBA, which is instead characterized by only ANA-related ancestry A.⋅ New genetic groups: Afanaseivo_Mongolia(2), Chemurchek_southAltai(2), Chemurchek_northAltai(2)⋅ Published genetic groups: Afanaseivo(23), Okunevo_EMBA(19), and Baikal_EBA(5)Our dataset adds three main genetic groups during the Early Bronze Age: Afanasievo_Mongolia, Chemurchek_southAltai and Chemurchek_northAltai. We group two individuals from Shatar Chuluu site  into Afanasievo_Mongolia as both are archaeologically classified into the Afanasievo cultural context and genetically indistinguishable from Afanasievo individuals from the Russian Altai-Sayan region B.We group two individuals from Yagshiin Huduu site  into Chemurchek_southAltai as both are archaeologically classified to the Chemurchek cultural context and cluster together on PCA, providing the first genomic investigation of the Chemurchek culture. We observed that Chemurchek_southAltai has the highest genetic affinity to ANE-related groups  and secondary affinity to Iranian-related groups A and S5CWe find that Chemurchek_southAltai has a close genetic affinity to Dali_EBA A, an indThe Chemurchek_northAltai genetic cluster, consisting of two female Chemurchek individuals  from the northern Altai, shows high genetic affinity to ANA groups, with a small proportion of ancestry consistent with Chemurchek_southAltai B. Like o⋅ New genetic groups: Altai_MLBA(7), Ulaanzuukh_SlabGrave(11/16), UAA001(1), KHI001(1), UUS001(1), KHU001(1) and TSI001(1)⋅ Published genetic groups: Khövsgöl_LBA(17), ARS017(1), ARS026(1), Sintashta_MLBA(37), Krasnoyarsk_MLBA(18)Our dataset adds two main genetic groups in the Eastern Steppe during the MLBA - Altai_MLBA and Ulaanzuukh_SlabGrave - to the previously published Khövsgöl_LBA from northern Mongolia . Our newThe other genetic cluster, Ulaanzuukh_SlabGrave, contains 11 individuals with Ulaanzuukh burial type  and 5 individuals with Slab Grave burials (see below), from eastern Mongolia. They all are classified into one single genetic group given their strong genetic homogeneity with ANA C and thep-value = 0.056; p-value = 0.117; We also identified three outliers which do not fall into any of the three genetic clusters described above. UAA001 (Mönkhkhairkhan) from the Altai is well-fitted with 3-way admixture model using Afanasievo, Baikal_EBA and Gonur1_BA C, despit⋅ New genetic groups: Chandman_IA(9), Ulaanzuukh_SlabGrave(5/16),⋅ Published genetic groups: Tagar(8), CentralSaka(6), TianShanSaka(10), Kazakhstan_Berel_IAOur dataset adds two main genetic groups during EIA: one represented by Ulaanzuukh_SlabGrave and the other represented by the site of Chandman Mountain associated with the Sagly/Uyuk culture (Chandman_IA). In addition to the 11 Ulaanzuukh burials described above, four Slab Grave individuals  from eastern Mongolia also presented a homogeneous genetic profile with Ulaanzuuk and thus were merged into the Ulaanzuukh_SlabGrave analysis group C. IntereThe contemporaneous Chandman_IA from the Altai-Sayan region in western Mongolia has a genetic profile that matches the preceding Altai_MLBA cline. Since all individuals are from a single site and cluster together on PCA, we group them into a single analysis unit (“Chandman_IA”). Here, we use the Andronovo-associated dataset Krasnoyarsk_MLBA as the representative central steppe_MLBA group for admixture modeling because it is geographically closest to our test EIA groups. We first tested a 2-way admixture model of Krasnoyarsk_MLBA + Baikal_EBA, but it failed to adequately model the Chandmand_IA cluster, as did Karsnoyarsk_MLBA + Khövsgöl_LBA. Further changing the steppe_MLBA source from Karsnoyarsk_MLBA to Sintashta_MLBA did not rescue the 2-way admixture model. We then attempted a 3-way admixture model by adding Iranian-related ancestry as the third source, using a BMAC group from the Gonur Tepe site (Gonur1_BA) as a proxy. Using Krasnoyarsk_MLBA as the Steppe proxy, we observed 51.3% of Steppe, 42.2% of Baikal_EBA and 6.5% of Iranian ancestry in Chandman_IA D.a priori quite unlikely due to a long-distance migration from Bactria/Iran specific to Chandman_IA, we next applied the same 3-way models of Krasnoyarsk_MLBA/Sintashta_MLBA+Baikal_EBA+Gonur1_BA to four Iron Age central Asian groups  and also to the Final Bronze Age group Karasuk. We observed that Iranian-related ancestry proportions range from ∼7%–28% in the tested Iron Age groups, while not required for Karasuk. In particular, the Tian Shan Saka, geographically closest to the Gonur Tepe site, has the highest amount of estimated Iranian-related ancestry. Because of cultural connections between the Sagly/Uyuk of Chandman_IA and the Saka generally (see section 2.4 above), it is possible that Saka and related groups in Tian Sian, Fergana and Transoxiana/Turan (such as the sampled Tian Shan Saka) are the proximal source of the Iranian ancestry in the Iron Age groups further to the north, such as Chandman_IA. To narrow down the spatiotemporal origin of this Iranian-related ancestry, we tested 3-way models using alternative Iranian-related groups as the proxy in the Tian Shan Saka: (1) three other post-BMAC groups  that fall into the BMAC genetic cluster with Gonur1_BA  Shaonur1_BA E. TherefFor the prehistoric genetic groups described above, we used DATES to estimate the date of admixture between Western ancestry sources (WSH or the Iranian-related groups) and local ancestry sources  . As show⋅ New genetic groups: earlyXiongnu_west(6), earlyXiongnu_rest(6), SKT007(1), lateXiongnu(24), lateXiongnu_sarmatian(13), lateXiongu_han(8), TAK001(1), TUK002(1)⋅ Published genetic groups: Xiongnu_WE(2), Xiongnu_royal, Han_2000BP(2)Our dataset reveals a great deal of previously uncharacterized genetic diversity during the Xiongnu period. For individual modeling, we tested every possible combination of five main ancestries: Steppe , Gonur1_BA, Khövsgöl_LBA, Ulaanzuukh_SlabGrave, and Han. Considering the low resolution of individual modeling, we report selected working models that work for many individuals belonging to the same time period and archaeological context and that reflect qualitative trends observed in PCA. We observed that Iron Age Chandman_IA is a good Steppe ancestry proxy for many Xiongnu individuals, but there are also many who have western Eurasian ancestry in higher proportion than that of Chandman_IA. These individuals with high western Eurasian ancestry proportion show strong affinity to the Iranian-related ancestry that cannot be explained by the earlier Late Bronze Age steppe groups . Instead, Gonur1_BA or Iron Age Sarmatian fit better with the genetic profile required. Also, a few individuals fall into the eastern Eurasian cline along PC2 and are explained as a combination of the eastern Eurasian gene pools, Ulaanzuukh_SlabGrave and present-day Han Chinese, without contribution from western Eurasian sources F. We useFor the group-based qpAdm modeling, we split Xiongnu into two categories based on their age - early Xiongnu and late Xiongnu. We further split early Xiongnu into two subgroups, earlyXiongnu_west  and earlyXiongnu_rest , based on their individual modeling results, leaving out one individual outlier - SKT007 (Khövsgöl_LBA-like). The two previously published Xiongnu individuals grouped as “Xiongnu_WE” show a similar genetic profile to earlyXiongnu_rest, are dated to the early Xiongnu period, and are from the same valley as the two early Xiongnu sites (SKT and AST) in our dataset F. For th⋅ New genetic groups: TUK001(1), earlyMed_Türk(7), TUM001(1), earlyMed_Uyghur(12), OLN007(1)Our dataset adds two main genetic groups during the early Medieval period in Mongolia: earlyMed_Türk and earlyMed_Uyghur. For each individual, we tested every possible combination of four main ancestries: Steppe , Gonur1_BA, Ulaanzuukh_SlabGrave, and Han. The genetic contribution from Iranian-related ancestry becomes even more prominent in Türkic and Uyghur individuals, as seen from well-fitted models using the Alan, an Iranian pastoral population from the Caucasus H. OveralWith respect to Uyghur burials, many consist of collective graves, and it has been suggested that such graves may contain the remains of kin groups . We exam⋅ New genetic groups: lateMed_Khitan(3), lateMed_Mongol(61), SHU002(1)Our dataset adds two main genetic groups during late Medieval in Mongolia: lateMed_Khitan and lateMed_Mongol. We used the same modeling strategy as used for the early Medieval period, and additionally explored the genetic cladality between every individual from the Mongol period and from modern Mongolic-speaking populations via qpWave J. Relati


A subset of 148 patients  completed study surveys to assess uncertainty, distress, and patient experience. Responses were similar between both models. Healthcare providers survey responses indicated they believed the oncology clinic-based model was acceptable and a positive experience. Oncology clinic-based genetic testing using a multi-gene panel approach and post-test counselling with a genetic counsellor significantly reduced wait times and is acceptable for patients and health care providers.New streamlined models for genetic counseling and genetic testing have recently been developed in response to increasing demand for cancer genetic services. To improve access and decrease wait times, we implemented an oncology clinic-based genetic testing model for breast and ovarian cancer patients in a publicly funded population-based health care setting in British Columbia, Canada. This observational study evaluated the oncology clinic-based model as compared to a traditional one-on-one approach with a genetic counsellor using a multi-gene panel testing approach. The primary objectives were to evaluate wait times and patient reported outcome measures between the oncology clinic-based and traditional genetic counselling models. Secondary objectives were to describe oncologist and genetic counsellor acceptability and experience. Wait times from referral to return of genetic testing results were assessed for 400 patients with breast and/or ovarian cancer undergoing genetic testing for hereditary breast and ovarian cancer from June 2015 to August 2017. Patient wait times from referral to return of results were significantly shorter with the oncology clinic-based model as compared to the traditional model (403 vs. 191 days;  Emerging use of genetic testing to guide cancer therapies, combined with greater public and health care provider awareness, has led to rising demand for publicly funded cancer genetic services ,2. This BRCA1 and BRCA2 can lead to psychological distress , De, De18], Oncologists and genetic counsellors were invited to complete surveys to assess feasibility and acceptability of the streamlined model. Oncologists were invited to complete two questionnaires to evaluate their experiences and acceptability: Oncologist Questionnaire  13] and and13] aData for wait times from referral to HCP to return of genetic test results, indication for genetic testing, method of genetic testing, patient demographics, and personal and family cancer history were obtained through review of the BC Cancer and HCP electronic chart and pedigree databases. Descriptive statistics were presented for both categorical and continuous variables. Categorical variables were summarized as frequencies with percentages, and continuous variables were described as means with standard deviations. Fischer’s exact test was used to determine if there were demographic differences in the patient population proportions between the two clinical models. All summarized questionnaire means were calculated using the recommended practices in the original validations of the questionnaires.t-test was used to compare wait times for the two clinical models. Generalized linear model analyses were performed to compare psychological outcomes. Missing survey data was replaced using questionnaire specific scoring instructions. The MICRA sub-scales of Distress and Positive Experiences were dichotomized due to an over-abundance of zero answers from patients. All Decisional Conflict Scale sub-scales were transformed into a categorical variable with three levels due to non-normality. To explore associations between patient factors and genetic test results with survey scores, bivariate analyses using linear or logistic models were performed. Multiple linear or logistic regressions were used to examine the impact of the genetic counselling models on survey scores after adjusting for the significant factors identified in the bivariate analysis. A log transformation was applied for continuous outcomes if needed. After adjusting for multiple comparisons using the Bonferroni method, a p-value of 0.003 was used to indicate significance for all analyses. Statistical analysis was performed using R V.3.5.1.An independent sample In summary, we conclude that streamlined oncology clinic-based genetic counselling and genetic testing resulted in similar outcomes for patient reported outcome measures, was acceptable to their health care providers, and significantly reduces wait times for genetic testing. Our study is unique as genetic testing was performed in a patient population with different cancer types (breast and/or ovarian cancer) while using a multi-gene panel approach, with all VUS returned to patients, in a publicly funded population-based health care system. Efforts to further expand and evaluate the oncology clinic-based model are underway, with recent expansion to include additional oncologists, nurses, nurse practitioners, and general practitioners with oncology expertise. Future directions include implementing surveys online, implementing pre- and post-genetic counselling surveys as part of clinical care to measure empowerment, and to evaluate patient reported outcome measures and clinician acceptability when introducing new models. As well, future directions will increase testing to include hereditary cancer syndromes other than HBOC.The field of cancer genetic counselling is rapidly evolving due to recent advances in personalized cancer treatments for patients with hereditary cancer. Multidisciplinary development and evaluation of new rapid genetic testing approaches are needed prior to adopting these into routine clinical practice.


R2 = 0.826–0.887, N = 330). (2) We use this R-B matrix to simulate an outbreak scenario in 22 immigrant cities in China, and propose risk prevention measures after the outbreak. If similar scenarios occur in the cities of Wenzhou, Guangzhou, Dongguan, or Shenzhen, the disease transmission will be wider. (3) We also use a matrix to determine that cities in Henan province, Anhui province, and Municipalities  in China will have a high risk level of disease carriers after a similar emerging epidemic outbreak scenario due to a high influx or outflow of migrant populations.The outbreak of COVID-19 in China has attracted wide attention from all over the world. The impact of COVID-19 has been significant, raising concerns regarding public health risks in China and worldwide. Migration may be the primary reason for the long-distance transmission of the disease. In this study, the following analyses were performed. (1) Using the data from the China migrant population survey in 2017 , a matrix of the residence–birthplace (R-B matrix) of migrant populations is constructed. The matrix was used to analyze the confirmed cases of COVID-19 at Prefecture-level Cities from February 1–15, 2020 after the outbreak in Wuhan, by calculating the probability of influx or outflow migration. We obtain a satisfactory regression analysis result ( Coronavirus, widely distributed in humans, other mammals, and birds, can cause respiratory, intestinal, hepatic, and nervous system diseases . Due to chunyun”) [The migrant population outflow from the outbreak center in immigrant Wuhan city, which accommodates 5.103 million migrants, is a factor that is responsible for the transmission of the disease to other regions. In China, there is a Spring Festival travel rush (“hunyun”)  every yeNetworks and the epidemiology of directly transmitted infectious diseases are fundamentally linked . NetworkThrough network research, prediction is essential to prevent the transmission of disease in new regions in the future. We asked the following two questions: (1) What are the consequences of similar emerging epidemic occurring in other immigrant cities? (2) How can policies for similar emerging epidemic scenarios be formulated? Based on these questions, we used the China Migrant Population monitoring data 2017 (CMDS 2017)  to construct a network called Residence place–Birthplace probability matrix (R-B matrix) to predict migrant influx and outflow migration. An empirical study was then performed using COVID-19 transmission as an example, and compared the advantages of using the R-B matrix with population or population density to predict disease transmission. Then, we simulated the outbreak scenario in the immigrant cities with the highest migrant population in China, and put forward some risk prevention measures after an emerging epidemic outbreak in the future.In 2017, the National Health and Family Planning Commission of China (NHFPC) conducted a CMDS throughout China. The sample points were distributed in 330 prefecture-level cities in 31 provinces. Interviewees were the migrant population residents who had lived in the inflow residence places for more than a month, did not have household registration in that city, and were at the age greater than 15 by 2017. Those whose current residence was different from the place of their household registration in the same city were not included. According to the survey plan, 170,000 domiciles were to be interviewed, and 169,989 were actually interviewed, involving 667,122 cohabited members, of which 575,288 cohabited members were in the inflow places. The monitoring included obtaining basic information on the migrant family, housing, basic public health, places of household registration, and so on. This survey includes the latest and widest data on the migrant population in China, and the results of the survey include well-represented urban groups and key cities.The confirmed disease data for prefecture-level cities came from the National Health Commission of China and provincial health commissions. We counted the number of confirmed cases in 330 prefecture-level cities during February 1–15, 2020, and this number reported by provinces in mainland China did not include Hong Kong, Macao, or Taiwan. Moreover, the data for the number of cases in these cities and the city names in the survey data for the CMDS 2017 were matched at the prefecture level. The number of people infected in different prefecture-level cities will also be affected by cofactors. In addition to the CMDS data, we also incorporated the population data and population density of prefecture-level cities that is commonly used in traditional infectious disease network research as cofactors to indicate the infective host density and total host population ,27. ThesFirst, we used the migrant population’s residence place and their household registration data from the survey data to construct the migrant Residence–Birthplace number matrix to model the migration network of migrants . The resi and living in node city j in the survey data; m denotes the number of cities, and there are a total of 330 cities in this study. Because the sample of the migrant population that obtained residence registration was excluded, the When we constructed the matrix, we deleted the following population samples: first, the local population that obtained a household registration; second, the population that lives elsewhere. By this method, the migrant population sample can well reflect the birthplace of a certain migrant population, with a total sample size of 432,907. The Residence–Birthplace number matrix formed is as shown in Equation (1):i whose birthplace was node city j.j, among the migrant populations whose birthplace was node city i. Taking Wuhan as an example, we can see that the variables related to the migration of migrants in Wuhan are j = 1 to m).Then, we standardized the probabilities of each residence place, adding up to 1 for each column or row, to form a probability matrix called the R-B matrix that illustrates how the migrant population in each city migrated from some birthplaces: j on day t after an outbreak occurred in city a. j city and is used to indicate the infective host density, j city and is used to represent the host population [a and j, respectively (Equation (3)).We initially considered constructing a model to verify whether the R-B matrix and cofactors can predict transmission of the disease at Prefecture-level Cities (Equation (3)).pulation ,27, and a in the equation is Wuhan, j denotes the 329 prefecture-level cities in China except Wuhan, and t denotes the 15 days starting from February 1, 2020. We performed a bivariate correlation analysis between the r) was used to test these correlations and to determine which correlation was stronger. Using the number of diseases in prefecture-level cities 15 days after the outbreak of COVID-19 in 2020 as an example, the city Based on the correlation analysis in Finally, because the location of the next emerging epidemic outbreak is uncertain, we also conducted an analysis of the highest risk of the 330 prefecture-level cities based on the risk map, and calculated the total risk values of other cities in the top 22 largest immigrant cities after the outbreak. By using the risk ranking of disease transmission in prefecture-level cities, it is evident that greater priority should be given to the formulation of emergency response policies in these cities.We used places of residence and household registration data from the CMDS 2017 data to construct an R-D matrix. The distribution of confirmed cases of COVID-19 infections (r) for r of The Pearson correlation coefficients , and therefore, we will further use these models to the risk level according to It can be seen that the model in According to the results of prediction of risk level in Most of the differential spatial transmission of COVID-19 in China is due to migrants returning from affected regions. Long-distance transmission of COVID-19 at this time, from cities such as Harbin and Wenzhou, is due to the large numbers of migrants from Wuhan in these cities and the probability that the migrants will engage in long-distance travel. A large number of studies used the number of transport networks to examine the possibility of transmission outbreaks. However, this method is inaccurate in predicting migrants’ homecoming in China. The transportation used for returning home in China in the spring may consist of multiple forms, including self-driving, which may be different from other forms of migration. Therefore, it is not feasible to use only a single transportation method to make predictions. China is a country undergoing rapid urbanization, and during this process, there is a large migrant population. In this study, we take advantage of the important characteristic of migration in the Spring Festival travel rush with a clear direction, that is, returning to the birthplace, and propose the idea of using the information of residence place and birthplace in the migrant population questionnaire to build a Residence place–Birthplace probabilities network. The bivariate correlation analysis and regression results in Because of the characteristics of human networking, the transmission of disease is spatially heterogeneous. Spatially localized mass treatment is a crude approach compared to detailed contact tracing ,32,33, bFor cities not in the disease region, when there is a disease situation in other cities, we need to consider the origin of immigrants from different places by Accurate geographic migration networks enable early and efficient planning of interventions to make the best use of scarce resources or manpower. We identified these high-risk node cities in The following emergency plans need to be prioritized in these important node regions: 1) Plans for the deployment of vaccines, the provision of detection reagents, the provision of protective equipment , and the provision of disease prevention personnel ,33 will  Plans foThe outbreak of COVID-19 in China has attracted wide attention from all over the world. The impact of COVID-19 has been significant, raising concerns regarding public health risks in China and worldwide. A large migrant population left Wuhan and returned to their hometowns for the Spring Festival, which resulted in the transport of a large amount of virus to other regions. In this study, we constructed a matrix of the residence–birthplace (R-B matrix) of migrant populations by using the data from the China migrant population survey in 2017. This matrix was used to analyze the confirmed cases of COVID-19 from February 1–15, 2020 after the outbreak in Wuhan. Then, we used this R-B matrix to simulate an outbreak scenario in 22 immigrant cities in China, and proposed risk prevention measures after the outbreak. It was found that similar emerging epidemic scenarios would occur in the cities of Wenzhou, Guangzhou, Dongguan, or Shenzhen, with a wider disease transmission. We also used the matrix to determine that cities in Henan province, Anhui province, and Municipalities  have a high risk level of disease carriers after the outbreak due to a high influx or outflow of people. Thus, we should give priority to formulating policies that will ensure that an emergency response plan is launched first after the occurrence of a similar emerging epidemic outbreak to check, control, and isolate people who leave or return after the outbreak. Our method has several limitations. First, the method in this study was only obtained from the perspective of the migrant population, but a small proportion of the registered population also returned to their birthplace during the Spring Festival after the outbreak of the disease. Second, COVID-19 was transmitted by cross-provincial, cross-city, and trans-national travel. In addition, inadequate criteria for early outbreaks may have contributed to some of the errors in the number of people diagnosed, and therefore, further research is needed. However, the migrant population contributed greatly to disease spread, and we believe that limiting migration and isolating people is one of the most effective ways to control an emerging epidemic. Our suggestions are very helpful and relevant, and would assist the Chinese government in its future management of disease. Priority should be given to research on the migration places of migrant populations in immigrant cities so that targeted policies can be formulated, especially population control during the Spring Festival. Simulation of the transmission of secondary infections in migrant birthplaces can also contribute to long-term infection research. In addition, it is necessary to model the number of people infected at different times.In summary, this study provides a new perspective for the examination of epidemiological and behavioral data by proposing migrant networks. We have shown that we did not analyze traffic data , but rather, we analyzed the social network causes of traffic migration. This is more predictive, and even in complex situations, simple tools can be designed to provide a method for predicting the transmission of disease and measuring risk. It is hoped that by providing simple and accurate approximations, a constantly evolving understanding of the complexity of the real world will be gained, and the method can continue to assist in understanding and controlling diseases.


Homarus gammarus) individuals caught around Corsica and tested whether they showed Atlantic ancestry. Due to a natural marked phylogeographic break between Atlantic and Mediterranean lobsters, we hypothesized that lobsters with dominant (>0.50) Atlantic ancestry were descended from historical stocking releases. Twenty Corsican lobsters were genotyped at 79 single nucleotide polymorphisms, and assignment analysis showed that the majority (13) had dominant Atlantic ancestry. This suggests that the hatchery stocking program carried out in Corsica during the 1970s, using individuals translocated from the Atlantic coast of France, has likely augmented local recruitment but at a cost of altering the genetic structure of the Corsican lobster population.Stocking is often used to supplement wild populations that are overexploited or have collapsed, yet it is unclear how this affects the genetic diversity of marine invertebrate populations. During the 1970s, a lobster stock enhancement program was carried out around the island of Corsica in the Mediterranean using individuals translocated from the Atlantic coast of France. This included the release of thousands of hatchery‐reared postlarval lobsters and several adult individuals, but no monitoring plan was established to assess whether these animals survived and recruited to the population. In this study, we sampled European lobster ( Stocking is often used to supplement wild populations that are overexploited or have collapsed, yet it is unclear how this affects the genetic diversity of marine invertebrate populations. In this study, European lobsters caught around Corsica in the Mediterranean were genotyped and the results showed that a large proportion of lobsters have dominant Atlantic ancestry. This suggests that the stock enhancement programme carried out in Corsica during the 1970s, using individuals translocated from the Atlantic coast of France, has likely augmented recruitment but at a cost of altering the genetic structure of the Corsican lobster population. Given tt Figure . Such inCenturies of overexploitation have led to collapses in European lobster stocks across the Mediterranean ; Formal analysis ; Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Charlie Ellis: Conceptualization ; Formal analysis ; Writing‐review & editing . Eric Durieux: Conceptualization ; Data curation ; Writing‐review & editing . Jean‐José Filippi: Data curation ; Writing‐review & editing (supporting). Jérémy Bracconi: Data curation ; Writing‐review & editing (supporting). Jamie Stevens: Conceptualization ; Funding acquisition (lead); Writing‐review & editing .Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


Bufo bufo along the Italian peninsula. We combined population genetic, phylogeographic and species distribution modelling (SDM) approaches to map ancestral areas, glacial refugia, and secondary contact zones. We consistently identified three phylogeographic lineages, distributed in northern, central and southern Italy. These lineages expanded from their ancestral areas and established secondary contact zones, before the last interglacial. SDM identified widespread glacial refugia in peninsular Italy, sometimes located under the present-day sea-level. Generalized linear models indicated genetic admixture as the only significant predictor of the levels of population genetic diversity. Our results show that glacial refugia contributed to preserving both levels and patterns of genetic diversity across glacial-interglacial cycles, but not to their formation, and highlight a general principle emerging in Mediterranean species: higher levels of genetic diversity mark populations with substantial contributions from multiple genetic lineages, irrespective of the location of glacial refugia.Genetic diversity feeds the evolutionary process and allows populations to adapt to environmental changes. However, we still lack a thorough understanding of why hotspots of genetic diversity are so 'hot'. Here, we analysed the relative contribution of bioclimatic stability and genetic admixture between divergent lineages in shaping spatial patterns of genetic diversity in the common toad  Levels of genetic diversity have been associated to the extinction risk, and have been estimated to be 30% lower in threatened species than in their non-threatened relatives5. Moreover, genetic diversity within populations can drive ecological dynamics shaping biological communities and ecosystem functions (see6 for a review). For instance, some recent studies8 found significant positive effects of genetic diversity in plant populations on species richness, abundance, and productivity of associated biological communities, with important implications in applied sciences, such as ecological restoration9.Intraspecific genetic variation feeds the evolutionary process and affects biodiversity patterns at all levels of biological organization. It provides populations with the potential to adapt to changes in their biotic and abiotic environment10. A major research arena has involved the identification of hotspots of intraspecific genetic diversity, that is, geographic regions harbouring exceptionally high diversity12. These hotspots are increasingly recognised as key targets in conservation biology14, and their correct identification is an important step in designing effective strategies for the long-term persistence of populations in the face of global change15.Given the importance of genetic diversity within populations, the analysis and interpretation of spatial patterns of variation across species ranges have been a long-lasting endeavour in evolutionary biology16. This widespread geographic association between refugia and hotspots of biodiversity has classically been viewed as evidence for a causal link between prolonged bioclimatic stability and high levels of intraspecific genetic diversity17. Additionally, hotspots may also result from secondary contact and admixture between intraspecific lineages, differentiated within sub-refugia during periods of unfavourable climatic conditions17. Under the latter scenario, hotspots of genetic diversity would in fact be melting-pots19. Although they were originally treated as alternative11, prolonged bioclimatic stability and secondary contact and admixture are not mutually exclusive scenarios for the formation of hotspots of intraspecific genetic diversity. In fact, substantial evidence has been gathered in favour of each scenario (e.g.22) with different levels of genetic diversity that can be explained for different populations by two factors: distance from putative refugia and extent of admixture24. However, the relative contribution of the two factors to the formation of spatial patterns of genetic variation, and particularly of hotspots, remains poorly explored (but see22).Despite their theoretical and applied importance, we still lack a thorough understanding of why hotspots of genetic diversity are so 'hot'. In more than thirty years of phylogeographic and population genetic investigations, hotspots of genetic diversity have often been observed in close geographic association with major Pleistocene glacial refugia. For example, southern European peninsulas, south-western and south-eastern North America, and tropical Australia have been identified as major glacial refugia as well as hotspots of biodiversity, at both species and intraspecific levelsBufo bufo in peninsular Italy, a major hotspot of genetic diversity for the species26. The common toad is widely distributed in temperate habitats of the western Palearctic and range-wide phylogeographic studies have identified three divergent mtDNA lineages of the common toad in the Italian peninsula27. One lineage is restricted to southern Italy and Sicily, a second one ranges from central to north-central Italy and a third one is distributed in northern Italy and neighbouring areas. In this study we aimed to dissect the spatial patterns of genetic variation of B. bufo populations in its Pleistocene Italian refugium. We combined population genetic and phylogeographic tools with species distribution modelling, in order to (i) assess the fine-scale population genetic structure and diversity of B. bufo in the Italian peninsula; (ii) infer the location of ancestral areas, glacial refugia and secondary contact zones, and (iii) investigate the relative contribution of prolonged habitat stability and admixture between lineages in the formation and evolution of the Italian hotspot of genetic diversity for the species.Here, we address this question by analysing the contribution of bioclimatic stability and admixture following secondary contact in shaping the fine-scale spatial patterns of genetic diversity in the common toad Bufo bufo individuals from 70 sampling localities: a 722-bp fragment of the Cytochrome B gene (CytB) and a 517-bp fragment of the mitochondrial 16 s rRNA gene. In the combined mtDNA dataset (1239 bp), we found 83 different haplotypes defined by 160 variable positions. Two haplotypes found in nine individuals from three localities  were identified as belonging to Bufo spinosus  and in the Ligurian Alps .The phylogenetic network outlines four main ure Fig. : a northB. bufo mtDNA lineages. For each lineage, runs converged to a stationary distribution and had satisfactory Effective Sample Size (ESS) values (> 200). The ancestral areas of the four lineages were mapped in distant regions along the Italian peninsula  to 54 (Bspi 4.29). Allelic richness and mean expected heterozygosity estimates for each population are presented in Table A total of 500 individuals from 57 populations was genotyped at nine microsatellite loci, with 3.8% of missing data. Locus r Bspi 3.1 to 54  versus the number of clusters (K) reached a plateau at K = 4, indicating that four main genetic clusters occur in the study area . Bar-plots showing individual admixture proportions and pie-charts showing the average proportion of each cluster in each sampled population are presented in Fig. B. bufo and B. spinosus was geographically restricted to their area of close proximity in the Ligurian Alps.TESS analyses revealed strong genetic structure in Italian rea Fig. . The spaWe obtained 581 species occurrences for the central lineage and 256 for the southern lineage (Supplementary Table The variance inflation factor (VIF) analysis selected seven variables for the central lineage and six variables for the southern lineage  are summarised in Table 28, mitochondrial and microsatellite markers consistently identified three main lineages of Bufo bufo, ranging in southern, central and northern Italy. The phylogeographic boundaries between lineages, although sharper with mtDNA than with the microsatellite markers, coincide with two well-known suture zones of the Italian peninsula: the lower Volturno-Calore river basin and the northern Apennines12. The extent of phylogeographic concordance with previously studied organisms from the same area is substantial, in terms of the co-distribution of intraspecific lineages37. However, the spatial pattern of genetic variation observed within B. bufo populations shows some remarkable features, that may well exemplify the disparity between co-distribution of phylogeographic lineages and correlated population histories.In line with previous studies34). However, the spatial distribution of genetic variation within the range of the southern lineage of B. bufo contrasts with that found in other species studied with enough sampling depth, typically comprising finer population structure and multiple population units arranged along the south-north axis38. The south of the Italian peninsula has been recurrently identified as a hotspot of intraspecific genetic diversity31. Conversely, neither mtDNA nor microsatellites provided evidence for further population structure within the southern lineage of B. bufo, and, accordingly, the southernmost area of the peninsula is rather a ‘cold spot’ of genetic diversity for this species . The absence of population structure of the extant B. bufo lineage in southern Italy may result from two processes. Either the range expansion of the extant southern lineage swamped all other pre-existing population units, or these went extinct before this range expansion  co-distributed with the central lineage of B. bufo, (ii) with closest affinities with lineages in southern Italy, and (iii) not showing evidence of further population sub-structure within this area. Whether these multiple lines of phylogeographic concordance are genuine realisations of correlated population histories in response to paleoenvironmental changes requires further research.The geographic distribution of the central lineage parallels the distribution of intraspecific lineages in other organisms, including reptiles27), appears in concordance with the distribution of several phylogeographic lineages sandwiched between the Alps and the northern Apennines44. However, in B. bufo this probably involved a unique biogeographic route, encompassing two distinct colonisation events of the river Po plain, one from the east and one from the west. Indeed, this lineage is characterised by a single nuclear gene pool , but two distinct mtDNA sub-lineages, with western and central—eastern European distributions, respectively, coincident with the e2 and e3 lineages of25. Arntzen et al.27 suggested that the two colonisation events occurred after the LGM, eastward from a western refugium, and westward from the northern Balkans. Our results support this scenario, with some amendment. We modelled two distinct ancestral areas for the two mtDNA sub-clades, and we located one area in the Ligurian Alps, and one area close to the Venetian Prealps, respectively  and the substantial range variations that, according to our SDM analyses, affected the B. bufo lineages in peninsular Italy in the Late Pleistocene. As mentioned above, there is an increasing appreciation of the major role and long-term consequences of secondary contact and admixture processes in increasing the genetic diversity of populations, promoting the formation of hotspots, as well as the sharing of adaptive genetic material between divergent lineages. However, in the absence of barriers to gene flow between the interacting lineages, the spatial patterns associated to these historical processes are mostly seen as transient46. In fact, the scenario emerging from our results suggests that they might be not so ephemeral as usually thought.The GLMs Table  indicateB. bufo populations into separate glacial refugia along the south-north axis. Notably, much of this coastal strip is located within areas that are presently below the sea-level, indicating that it acted as a "true refugium" sensu Recuero & García-París47, that is, as an area previously unoccupied by the species, where lineages retreat once their range becomes unsuitable due to paleoclimatic changes.According to the estimated SDMs for the central and southern lineages at the LGM Fig. , a large47. Instead, throughout most of the peninsula, B. bufo populations likely moved from interior areas  toward coastal regions and back, in response to paleoclimatic oscillations, through small-scale range migrations that did not erase previously formed patterns of genetic variation. Under this scenario, glacial refugia contributed to preserving levels and patterns of genetic diversity across glacial-interglacial cycles, but not to their formation.Together with the very limited and highly fragmented distribution of the inferred areas of stability see Fig. , this sc50, or to continental areas where wide coastal lowlands opened following glacial sea-level drops52. In line with these previous studies, our SDMs for the LGM suggested a possible area of suitable bioclimatic conditions for the southern lineage at the southern edge of the wide coastal lowland now covered by the Adriatic Sea. However, as discussed above, we also found evidence of a narrow glacial refugium along the Tyrrhenian  coast, which is at the same time an unprecedented but not completely unexpected result. Indeed, paleoenvironmental reconstructions based on palynological, microfossils, and sedimentological data, show the existence of areas of high ecological stability along the western coastal plains of the peninsula54, where the effects of climate change were mitigated54. Our results suggest that the importance of these narrow coastal refugia for temperate animal species might have been overlooked in previous phylogeographic studies of co-distributed species51.Finally, our results extend previous findings concerning the possible location of glacial refugia within areas that are currently covered by the sea. Indeed, although the occurrence of such areas has already been postulated, they have been mostly linked to insular geographic settingsB. bufo in peninsular Italy the answer is they are because they have been melting pots. Contrary to our expectations, based on previous studies of co-distributed species, we did not find a hotspot of genetic diversity in the southernmost area of the Italian peninsula, a well-documented glacial refugium for a broad range of taxa. Instead, populations from this area were among the least variable. Overall, spatial patterns of population genetic diversity were linked with the extent of admixture between distinct intraspecific lineages rather than with climatically stable areas. This highlights a general principle emerging from the documentation of concordant phylogeographic patterns in species with disparate evolutionary histories in southern European peninsulas: higher levels of genetic diversity mark populations with substantial genetic contributions from multiple differentiated lineages, irrespective of their location regarding glacial refugia.Why are hotpots of genetic diversity so hot? From the perspective of the common toad Bufo bufo individuals from 78 sampling localities (Table We collected 563 es Table . Tissue CytB) and a 517-bp fragment of the mitochondrial 16 s rRNA gene. Amplifications were performed in a 15-μL reaction volume following protocols described in Recuero et al.26. Purification and sequencing of the PCR products were conducted by Macrogen Inc. (http://www.macrogen.com), using an ABI PRISM 3730 sequencing system (Applied Biosystems). Electropherograms were checked by eye using FinchTV 1.4.0 (Geospiza Inc.). All sequences were deposited in the GenBank database . We amplified by polymerase chain reaction (PCR) two mitochondrial DNA (mtDNA) fragments, a 722-bp fragment of the Cytochrome B gene , following previously described protocols55. Other markers known from the literature  were excluded from the analysis after trials based on 96 individuals showed inconsistent amplification in > 30% of the individuals analysed. Forward primers were fluorescently labelled and PCR products were electrophoresed by Macrogen Inc. on an ABI 3730xl genetic analyser (Applied Biosystems) with a 400-HD-size standard.Patterns of genetic variation in the nuclear genome were investigated with nine microsatellite loci  selected by the Smart Model Selection procedure57 under the Bayesian Information Criterion. The robustness of the topology was assessed via 1000 bootstrap pseudo-replicates. The estimated tree topology was then converted into a haplotype genealogy using Haplotype Viewer58.MtDNA sequences were aligned using GeneStudio Pro 2.2.0.0 . Phylogenetic relationships between mtDNA haplotypes were inferred using the maximum likelihood (ML) method implemented in PhyML3.10B. bufo genetic lineages and the spatial and temporal patterns of diffusion throughout their range were estimated using the Bayesian phylogeographic (BP) analysis in continuous space implemented in BEAST 1.859. To avoid any potential bias caused by population structure60, we performed separate analyses with the same settings for each main haplogroup identified by the previous phylogenetic analysis. We set the Bayesian skyline as coalescent tree prior61, MCMCs with length of 200 million generations sampling every 20 000 generations, “Cauchy” as spatial diffusion model62, a strict molecular clock model, and the fossil-calibrated substitution rate of 5.5 × 10–9 substitutions/year26. Geographical coordinates were provided for each individual, applying a jitter of ± 0.001° to duplicated coordinates. Trace files were inspected using Tracer 1.663 to evaluate the Effective Sample Size (ESS) of the estimated parameters, the appropriate burn-in, and the convergence between runs. Finally, the full posterior sample of trees was analysed in SPREAD 1.0.764, in order to estimate the ancestral area for each lineage (i.e. the geographic location of its most recent common ancestor).The ancestral areas of the main 65 was used to test for the presence of null alleles and large-allele dropout. Allelic frequencies, tests for deviations from the expected Hardy–Weinberg and linkage equilibria, and estimates of allelic richness  and the mean observed and expected heterozygosity (Ho and He) were computed using the diveRsity R package66, after excluding populations of sample size n < 4 in at least one locus .Microsatellite data were analysed using GENEMAPPER 4.1. Micro-Checker 2.2.367. The analysis was carried out by modelling admixture using a conditional autoregressive model, and consisted of 100 replicates for each K value (i.e. the number of clusters) between 2 and 10. Each replicate was 50 000 steps long, and the first 20 000 steps were discarded as burn-in. The spatial interaction parameter was kept at the default value (0.6), with the update option activated. The model that best fit the data was selected using the deviance information criterion (DIC). DIC values were averaged over 100 replicates for each value of K, and the best K value was selected as the one at which the average DIC reached a plateau. For the selected K value, the estimated admixture proportions of the 10 runs with the lowest DIC were averaged using CLUMPP 1.1.268.The population genetic structure was evaluated by means of the Bayesian clustering algorithm implemented in TESS 2.3.1, with the geographical origin of individuals as prior informationbiomod2 R package, following an ensemble forecasting approach69. To account for intraspecific variability72, we calibrated distinct SDMs for the central and southern lineages, because they are the only lineages whose geographic distribution has been fully resolved (see Results section). Occurrence data for the entire geographic distribution of each lineage were obtained by pooling field data collected during this study together with data from: Stoch73, Global Biodiversity Information Facility , Observado (www.observation.org). Occurrence data from Stoch73 were validated with our own field observations or by matching toponomy with topography and satellite imagery information, reaching precision ≥ 30 arcseconds (≈1 km at the equator); occurrence data from GBIF were verified by inspecting associated toad pictures. Duplicated records and data georeferenced with uncertainty ≥ 30 arcseconds (≈1 km at the equator) were removed.Locations of glacial refugia during the Last Glacial Maximum (LGM) were estimated with species distribution models (SDM) calibrated on the current climate and projected on the LGM. All SDMs were developed using the 74, we thinned the raw occurrences using the spThin R package75, obtaining five alternative calibration datasets for each evolutionary lineage. We thinned the occurrences considering the nearest neighbour distance expected in the study area with our sampling effort for a random sample . To limit the effect of sampling bias, we used a target-group approach to inform the selection of background data points76. We collected all data available for reptiles and amphibians available from the same references and for the same study area, obtaining a total of 12,360 unique records; these records were divided following the geographical distribution of each lineage and used in model calibration as background points.To limit spatial autocorrelation among occurrenceswww.worldclim.org77). We did not include in the analyses elevation data, which are highly collinear with temperature and would represent a problem for model transferability in time74, or NDVI , for which no corresponding layer exists back in time. To avoid issues linked to multicollinearity73, we performed a variance inflation factor (VIF) analysis, retaining only variables with VIF < 5. The same variables were also obtained for the LGM climate from the same database (at resolution 2.5′) according to three general circulation models .SDMs were calibrated considering the full set of 19 bioclimatic variables (current climate) obtained at 30-arc-second resolution from the WorldClim database (http:\\78). The final ensemble model was calculated as the AUC-weighted average of all models with AUC value greater than 0.779 and then projected under the current climate over the study area.For each lineage, we calibrated an ensemble SDM considering multiple initial conditions (the five thinned sets of occurrence points coupled with the set of background points and with the same climate layers) and different model classes . All models were validated using the occurrences excluded in the thinning; in particular we measured the area under the curve (AUC) of the receiver operating characteristic (ROC) and calculated the probability threshold which maximize the true skill statistics , subsequent analyses were carried out on the reduced dataset including only populations from the Apennine peninsula and Sicily .To explore the relative contribution of genetic admixture between divergent lineages and long-term bioclimatic stability within refugia to current levels of population genetic diversity, we used generalised linear models (GLM) carried out by means of the “glm” function in the 80, based on the number of genetic clusters  and their proportional contribution to the genetic make-up of each sampled population, as previously estimated by the TESS analysis.The extent of admixture within each population (i.e. sampling site) was estimated by calculating the Simpson’s diversity indexnear” in ArcGIS 10.1 (ESRI).In order to address the contribution of long-term bioclimatic stability to levels of population genetic diversity, we estimated two Euclidean distance measures: (i) distance of each sampled population to the nearest area of glacial refugium, as estimated by the SDM analysis; (ii) distance of each population to the nearest “stability area”, defined as a region of overlap between a glacial refugium and an ancestral area . The latter regions were considered as our best estimates for the areas of long-term bioclimatic stability (i.e. stability areas), since all the time windows available  support the species occurrence in these regions. However, it is worth mentioning that this procedure for the identification of stability areas might overestimate the occurrence of these areas, as in the case of repeated population extinctions and recolonizations in time-windows not covered by previous SDM and Bayesian phylogeographic analyses. Euclidean distances separating each studied population from these areas were computed using the function “Two series of GLMs with Gaussian distribution and identity link function were built, one using allelic richness and one using expected heterozygosity as dependent variables. GLMs were conducted using as predictors: Simpson’s index, the distance from the nearest refugium, the distance from the nearest stability area, as well as their interactions. Model comparisons were carried out by means of the Akaike Information Criterion.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary File S1.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.