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Mobility of middle Holocene foragers in the Cis-Baikal region, Siberia: Individual life history
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approach, strontium ratios, rare earth and trace elements
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Ian Scharlotta a,*, Andrzej Weber a
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a
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* Corresponding author: Tel. +1 780 492 9269;
[email protected]
Department of Anthropology, University of Alberta, Edmonton, AB, T6G 2H4, Canada
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Abstract
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Previous geochemical work conducted on the materials from the Khuzhir-Nuge XIV cemetery on
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Lake Baikal, Siberia, has demonstrated the effectiveness of using
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interpreting mobility patterns among Early Bronze Age hunter-gatherer groups. The research
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reported here focuses on six small cemeteries representing the Little Sea and Upper Lena micro-
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regions as well the Early Neolithic (EN) and Early Bronze Age (EBA) periods, thus expanding
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both the geographic and chronological scope of the previous work. The reference collection of
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environmental samples, to document bioavailability of the measured geochemical tracers, was
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also expanded substantially by inclusion of samples of modern plants and water from Lake
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Baikal and a number of surrounding rivers. First, second, and third molars of 14 adult individuals
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were tested for 87Sr/86Sr ratios as well as rare earth and trace element concentrations using LA-
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ICP-MS. Each human tooth was micro-sampled at four locations along the crown enamel thus
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providing data of higher temporal resolution relative to a single sampling locus. Geochemical
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signatures for water, plant and animal bone samples were found to be far more variable across
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the region than predicted based on the age and type of geologic formations.
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cultural micro-regions proved to overlap significantly and required trace element data to identify
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more discrete geochemical groups. The level of hunter-gatherer mobility between and within the
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analyzed micro-regions was found to be significant with individuals recovered from the Upper
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Lena showing contact with the Little Sea micro-region along the northwest coast of the lake.
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Sr/86Sr ratio analysis in
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Sr/86Sr ratios for
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Keywords: Strontium isotope ratios; trace elements; Lake Baikal; middle Holocene; hunter-
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gatherers; laser ablation 1
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1. Introduction
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Examination of strontium isotope ratios (87Sr/86Sr) in human and animal bones and teeth is a
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useful technique of gaining insights into migration and mobility patterns in past populations.
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Strontium isotope ratios broadly reflect the underlying bedrock geology, manifested in
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biologically available portions of the source materials (e.g., soils, plants, water, animals). Taken
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a step further, the question is whether a technique based roughly on differences between rather
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large geologic zones can be effective for tracking individual or group mobility on the landscape
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with resolution finer than migrations between such large areas. If so, such information would
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greatly benefit studies of prehistoric mobility, allowing for better informed discussions of where
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an individual came from and reaching beyond the question of whether they were born, lived and
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buried in the same locale.
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The analysis of
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Sr/86Sr isotope systems in skeletal tissues, while from the geochemical
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perspective rather robust as a technique, is complicated in that the processes by which strontium
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is transferred first from the ground to the diet and then to the skeleton are susceptible to
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influence and alteration by even subtle changes in diet and localized mobility (Bentley, 2006).
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These concerns are potentially less significant in the context of agrarian populations, or even
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pastoral groups which follow relatively fixed annual cycles, than in hunter-gatherers that can
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potentially experience diverse physical mobility and multiple dietary changes over the period of
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a single or several years.
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In the Cis-Baikal region of Siberia (Fig. 1), it has been hypothesized that hunter–gatherer
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groups formed centers with higher population densities near reliable food resources such as the
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productive fisheries on the Angara River and the Little Sea areas which feature a combination of
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riverine and cove-and-lagoon fishes, respectively. On the lake, the Baikal seal would be available
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in late winter to early spring pretty much everywhere along the open coast (Weber et al., 2011).
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It appears that the mobility of such groups was largely limited to those relatively small areas or
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micro-regions. Relocation of individuals between micro-regions took place as well but in a
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fashion that appeared to be somewhat asymmetrical: some micro-regions attracting more
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individuals than others. These insights come from examination of
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carbon and nitrogen stable isotope signatures in a few large Neolithic and Early Bronze Age
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(EBA) cemeteries in the Little Sea micro-region of Baikal (Khuzhir-Nuge XIV and Kurma XI),
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in the Angara valley (Lokomotiv and Ust’-Ida), the Shamanka II cemetery on the southwest
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Sr/86Sr values as well as
1
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coast of Baikal, and several other small cemeteries scattered around entire Cis-Baikal (Weber et
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al., 2011).
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The current study focuses on mapping the distribution of the biologically available 87Sr/86Sr
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ratios and rare earth and trace element concentrations throughout much of the Cis-Baikal region
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and on examination of these geochemical tracers in 14 foragers represented by molar samples
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recovered from several small cemeteries (Table 1). First, second, and third molars of 14 adult
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individuals were tested for
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using LA-ICP-MS (laser ablation – inductively coupled plasma – mass spectrometry). Each
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human tooth was micro-sampled at four locations along the crown enamel thus providing data of
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higher temporal resolution relative to a single sampling locus.
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Sr/86Sr ratios well as rare earth and trace element concentrations
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More specifically, the samples examined here come from a few locations in the Upper Lena
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valley, an area not tested previously but implicated as an important contact zone with groups on
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the northwest coast of Lake Baikal (i.e. Little Sea) (Weber et al., 2011) and two burial grounds in
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the Little Sea area: six individuals are Early Neolithic (EN) and eight are EBA in age. It is
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expected that both the environmental data and the results from these small cemeteries will
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provide useful guidelines for the more comprehensive interpretations facilitated by the large
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cemeteries. The more specific questions to address are: (1) To what extent is the asymmetric
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pattern of human migration between the Upper Lena and Little Sea documented for the
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individuals from the large Khuzhir-Nuge XIV (KNXIV) cemetery, also visible in cemeteries on
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the Upper Lena? and (2) What additional migration patterns can be identified among the
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concurrent EBA foragers on the Upper Lena?
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2. Cis-Baikal region
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2.1. Food resources
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The Cis-Baikal region of Siberia refers to the area including the western coast of Lake Baikal,
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the upper sections of the Angara and Lena river drainages, and the Tunka area adjacent to the
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southwestern tip of Lake Baikal (approximately 52-58°N and 101-110° E). Within this broader
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region archaeologists have traditionally identified three main micro-regions: the Angara valley
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from its source at Lake Baikal to Bratsk in the north, the Upper Lena from its source to Ust’-Kut
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in the north (however, most archaeological sites are located in the southern part of the area closer
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to Lake Baikal), and the Little Sea (or Ol’khon area) encompassing the Ol’khon Island and the 2
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adjacent mainland (Fig. 1). With more work done in the Irkut River Valley south of the East
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Sayan Mountains, that is between the southwest tip of Lake Baikal and Lake Hovsogol in
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Mongolia, a fourth micro-region, Tunka, is currently emerging.
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The topographic complexity of the rift valley that formed Lake Baikal led to the formation of
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a large number of micro-habitats, with a variety of seasonally available resources (Galazii, 1993;
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Weber, 2003; Weber et al., 2002). The thermal capacity of Lake Baikal moderates the local
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climate immediately along its coastline, resulting in milder temperatures during the winter and
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cooler temperatures during the summer. The Angara River Valley remains relatively free of
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snow during the long winter which attracts various species of ungulates looking for forage and
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less restricted mobility (Formozov, 1964). There is a variety of large and medium-size game
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found in the region including moose (Alces alces), red deer (Cervus elaphus), roe deer
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(Capreolus capreolus pygargus), reindeer (Rangifer tarandus), mountain goat (Capra sibirica),
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and wild boar (Sus scrofa). Small mammals, such as hare (Lepus sp.), marmot (Marmota
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sibirica), suslik (Spermophilus citellus), and waterfowl are also abundant in many areas around
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the lake. During the summer, large runs of black grayling (Thymallus arcticus baicalensis) and
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lenok (Brachymystax lenok) are found in the uppermost section of the Angara River near its
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source at Lake Baikal but otherwise the Angara and Baikal fisheries are entirely independent.
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Several fish species enter the tributaries of the Angara (Belaia, Kitoi, Irkut) in large numbers to
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spawn. The shallow coves and bays in the Little Sea also provide excellent fisheries and during
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the late winter and early spring, the Lake Baikal seal (Phoca sibirica) can be hunted on the ice
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along the open coast of the lake (Weber et al., 1998). Dietary studies of boreal forest populations
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report the use of mushrooms, berries, and pine nuts as vegetarian foods (Haverkort et al., 2010;
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Katzenberg and Weber, 1999; Lam, 1994; Marles, 2000), however, to date there is very limited
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archaeological evidence for the use of such foods during the Baikal’s middle Holocene.
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2.2. Geology
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Within Cis-Baikal there are four main geological zones that roughly correspond to
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archaeological micro-regions (Fig. 1): the Lake Baikal coast; the drainage of the upper Angara
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River; the basin of the Upper Lena River; and the Tunka Valley micro-region.
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In the upper Angara drainage, bounded by the Eastern Sayan Mountains to the west and the
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Central Siberian Plateau to the east and extending north towards Bratsk, the bedrocks are of 3
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Archaean–Proterozoic age consisting of granites, metamorphic schists and porphyritic volcanics
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at a depth of 2.3 km. Covering the basement are Cambrian sediments that consist mainly of
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dolomites with layers of limestones, rock, anhydrites, clays, sandstones, argillites, gritstones,
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marls and gypsum. The Cambrian material is covered by 100 m of Jurassic sediments made up of
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sandstones, siltstones and coal beds. Adding to this variation are the valleys connecting with the
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Eastern Sayan Mountains (e.g., Irkut, Kitoi and Belaia Rivers) drawing from mixed
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metamorphic, unmetamorphosed and magmatic complexes of Archaean and Early Proterozoic
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ages (Donskaya et al., 2008; Gladkochub et al., 2006). Various exposures of these bedrocks and
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Quaternary sediments, primarily clays, are expected to yield
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0.705–0.712 (Fagel and Boës, 2008; Haverkort et al., 2008; Shouakar-Stash et al., 2007).
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Sr/86Sr values in the range of
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The Upper Lena watershed, cutting through the Central Siberian Plateau as the river heads
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northwards, and the surrounding Central Siberian Plateau are underlain by Archaean–Proterozoic
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basement at around 2.2 km depth overlain by Cambrian and Precambrian sediments. These
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sediments consist primarily of dolomites interlayered with gypsum, anhydrite and halite rocks
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and beds of limestone and sandstone at different depths and beds of limestone and sandstone
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(Donskaya et al., 2008; Gladkochub et al., 2006). The thickness of the covering sediments and
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the relative geochemical homogeneity of the plateau yield expected 87Sr/86Sr values fairly tightly
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clustered around 0.709 (Huh et al., 1994; Huh et al., 1998).
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The Baikal coast geological zone includes the coastal areas of the lake as well as the Little
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Sea area enclosed by Ol’khon Island, the Baikal uplift zone, the Primorskii and Baikalskii
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mountain ranges. This area is characterized by relatively high
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due to the presence of Archean and Proterozoic granites (Donskaya et al., 2008; Galazii, 1993;
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Gladkochub et al., 2006). Values for Lake Baikal water are reported as 0.7085 (Kenison Falkner
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et al., 1992).
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Sr/86Sr (~0.720–0.735) values
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The Tunka micro-region consists of basement rocks from the Sayan–Baikal fold belt, also of
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Archaean and Proterozoic age. The Sharizhalgay uplift zone in the Eastern Sayan Mountains and
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bordering the Siberian Plateau includes metamorphic and magmatic complexes of similar age
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(Donskaya et al., 2008; Gladkochub et al., 2006). There is a diffuse zone of Cenozoic volcanism
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to the south and west of the southwest end of Lake Baikal, spanning from the East Sayan and
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Tuva to the Gobi and Mongolian Altai (Johnson et al., 2005; Rasskazov, 1994; Rosen et al.,
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1994). Furthermore, the area also features an essentially non-volcanic Late Oligocene– 4
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Quaternary sedimentary basin composing much of the eastern portion of the valley (Rasskazov,
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1994). All this yields a complex range and distribution of expected 87Sr/86Sr values in the Tunka
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micro-region, likely overlapping both values seen in the upper Angara drainage and the Baikal
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coast.
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3. Methodology
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3.1. Tooth mineralization
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This study is predicated on a few well-documented facts about tooth development. Enamel of
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human teeth, and of other mammals too, acquire their chemical composition via the diet during
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the times this hard tissue is undergoing mineralization. Each tooth crown develops during a
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specific time interval which is largely under genetic control (Avery, 1992; Hillson, 2002). For
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example, the crown of each of the three permanent molars represents approximately three–four
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years of developmental time: M1, from birth to ~three–four years; M2, between ~two–three and
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~seven–eight years; and M3, between ~seven–ten and ~twelve–sixteen years (Avery, 1992).
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Since tooth enamel does not undergo remodeling during the rest of the individual’s life and
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remains essentially chemically inactive, it retains the geochemical signatures ingested during
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those time intervals. Consequently, if during this entire period of time (i.e., from birth to ~
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twelve–sixteen years) a movement occurs to an area with different geochemical characteristics, it
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should be reflected in geochemical signatures of the examined tooth enamel. Thus, by comparing
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the composition of an individual’s enamel from teeth formed at different life stages and from
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different portions of the crown of the same tooth, it is possible to track mobility from infancy to
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late childhood (e.g., Beard and Johnson, 2000; Montgomery et al., 2000). What remains unclear,
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however, is the exact pattern of tooth mineralization.
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Teeth are dynamic mineral structures whose complexities are still being unraveled. It has long
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been recognized that the incremental striae of Retzius represent some aspect of matrix deposition
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complicated however by the delay between this matrix deposition and the final mineralization of
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the matrix (e.g., Brown et al., 1960). With the advent of micro-sampling techniques such as laser
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ablation and micro-drilling, there has been a resurgence of interest in the formation process of
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the incremental growth lines and the meaning of the information that can be recovered from
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these materials (e.g., Hillson, 2002; Horstwood et al., 2008; Kang et al., 2004; Prohaska et al.,
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2002; Richards et al., 2008; Scharlotta, 2012; Scharlotta et al., 2011). Numerous hypotheses 5
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have been forwarded regarding the pattern and progression of enamel mineralization, however,
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the common theme amongst all works is that the progression of mineralization of layers and/or
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maturation of matrices is patchy and effectively non-linear thus making the chronological
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relationship between incremental lines and geochemical signals tenuous (Bentley, 2006;
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Dolphin et al., 2003;
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Montgomery and Evans, 2006; Suga, 1982, 1989; Tafforeau et al., 2007).
Fincham et al., 1999;
Hillson, 2002, 2005;
Kang et al., 2004;
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Montgomery and Evans (2006), Fincham et al. (1999), and Scharlotta (2012) provide
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comprehensive discussion of the biomineralization of tooth enamel with respect to Sr isotope
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analysis. The process of mineralization spans a series of five distinct phases wherein an organic
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gel or protein superstructure is transformed into a mineral matrix: (1) secretion; (2) assembly; (3)
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matrix formation; (4) resorption prior to maturation; and (5) maturation (see Fincham et al., 1999
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Fig. 8; Bentley 2006 Fig. 18). Effectively, during all stages prior to maturation, the enamel
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remains an open chemical system vulnerable to alteration, overprinting, or averaging of the
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mineral matrix. Modern recovery techniques (e.g., laser ablation) can sample volumes of
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material small enough to be significantly biased by micro-scale heterogeneity in the sample. In
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spite of difficulties with a disjunction between enamel formation and maturation and potential
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averaging effects (Avery, 1992; Hillson, 2002), there is still promise to the method of micro-
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sampling tooth enamel. That the various growth layers in human teeth lines do not mineralize in
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a similar fashion of linear accretion has been well demonstrated; however, there are two general
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principles that remain valid: (1) the crown of a tooth will fully mineralize before the root; and (2)
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though accomplished in a patchy or wave-like fashion, there is broadly linear trend in
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mineralization of tooth enamel progressing from crown to cingulum.
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Ongoing research into this matter using herbivore teeth has demonstrated that there are long-
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term mixing effects in action during the formation and maturation of tooth enamel (Balasse,
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2003; Balasse et al., 2002; Britton et al., 2009; Brown et al., 1960; Hoppe et al., 1999, 2003,
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2004; Koch et al., 1995; Montgomery et al., 2010; Tafforeau et al., 2007). These studies have
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identified a secondary problem, namely the differences of residence time in the body for
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different elements and compounds. Water-soluble materials, such as oxygen, sodium and
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potassium, have a short residence time in the body of only 14 days. Elements that are not
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soluble, for example strontium, calcium, and lead can remain in the body for 800–1600 days,
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with 10% of traceable doses remaining active after 400 days (Bowen, 1979; Dahl et al., 2001; 6
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Montgomery et al., 2010). Recent works (e.g., Britton et al., 2009; Montgomery et al., 2007,
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2010) have demonstrated that this residence time in the body has an intriguing effect on isotopic
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signatures of a linearly-sampled herbivore tooth. Namely, an abrupt change in geochemical
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geography and/or diet will not manifest as a sharp transition in isotopic signals, rather there will
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be a gradual sloping change as contributions from different geochemical end-members vary
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within the body–water average signal.
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At face value, this combination of lag time in mineral formation and maturation with body-
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water averaging of over a year should render moot discussions of micro-sampling human teeth
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for interim provenance information between the crown and cingulum. It should however be
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noted that there is an important difference between herbivore and human teeth. While it is quite
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likely that the same mineralization primers are in effect for both human and herbivore teeth and
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that the non-linear progression of mineral maturation is effectively the same, the time spans
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involved are different. For example, each bovine molar will form and fully mineralize over a
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span of 12–18 months (Montgomery et al., 2010).
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theoretically span a time of 24–48 months between initial calcification and final mineral
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maturation, though it will likely occur in less than 36 months.
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comparative volumes and chronologies in discussing the differences between herbivore and
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human teeth. While many herbivore teeth are not good candidates for micro-sampling because
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their tooth formation rates will not outstrip uncertainties about residence time and mineralization
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rates, human teeth will likely exhibit some aspects of useful variability in isotopic signatures
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through formation time and thus through the enamel mineral structure.
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mind that while at present it is impossible to overcome the issue of residence time of dietary
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components and maturation time for the mineral matrix, it may still well be worth pursuing
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micro-sampling of human tooth enamel between cusp and cingulum.
Each human molar, however, can Thus, there is a gap in
It is useful to keep in
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3.2. Laser ablation on teeth
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Laser ablation (LA) studies involving skeletal materials began only recently, in part due to
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latent concerns about the risk of diagenetic alteration at microscopic scales of analysis. In the last
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decade or so, ICP-MS (inductively coupled plasma – mass spectrometry) tests on teeth and bones
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have increased (e.g., Bizarro et al., 2003; Budd et al., 1998; Copeland et al., 2008, 2010; Cucina
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et al., 2007; Horstwood et al., 2008; Koenig et al., 2009; Montgomery et al., 2010; Prohaska et 7
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al., 2002; Pye, 2004; Richards et al., 2008; Scharlotta, 2012; Scharlotta et al., 2011, 2013;
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Simonetti et al., 2008; Trotter and Eggins, 2006). ICP-MS and MC-ICP-MS (multiple collector
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– inductively coupled plasma – mass spectrometry) are generally faster and less labor intensive
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than traditional analytical methods, however, one of the trade-offs is the need for corrections for
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a variety of interferences. For Sr analysis, the largest, if not most pernicious problems are
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isobaric interference from 87Rb (rubidium) and a recently identified polyatomic interference from
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calcium phosphate (CaPO 4 ). Rubidium corrections are necessary for all ICP-MS and MC-ICP-
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MS analyses as the charged
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though this can be corrected with accurate mass-bias calculations. This is not a problem
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associated with sample introduction method. On the other hand, polyatomics such as Ca dimers
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and calcium phosphate species are notably absent in solution-mode analysis as sample ions are
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held in acid and thus prevented from recombining as they are free to do in the carrier-gas
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environment of laser ablation chambers.
87
Rb will carry the same mass-charge ratio as its
87
Sr counterpart,
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Woodhead et al. (2005), Simonetti et al. (2008), Horstwood et al. (2008), Vroon et al. (2008)
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and Scharlotta et al. (2011) have all discussed the presence of significant interference on mass 87
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from a previously unidentified source, thus impinging researchers’ ability to assess accurately
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the 87Sr/86Sr ratios of phosphate matrices with laser ablation. As all mammalian skeletal tissues
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are varieties of phosphate mineral matrices, this is a major problem in accessing the life signals
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contained therein. This also affects data analysis and reconstruction of the movement of such
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animals. At the root of the problem may be the excess of Ca and P present in the charged
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environment coupling with the oxide production rates within the MC-ICP-MS. In theory, Ca and
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P levels should be proportional in all parts of skeletal tissues, thus mineral replacements such as
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Sr and Ba (barium), and the incorporation of other trace elements should be proportional too, and
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interferences should be related to the oxide operational conditions of the instrument itself.
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Horstwood et al. (2008, 2011) and Scharlotta et al. (2011) demonstrated the close relationship
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between Sr concentration in samples and the resultant error in 87Sr/86Sr ratios and examined the
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nature of this relationship and the effectiveness of several correction factors to eliminate the
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adverse effects of polyatomic interferences.
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The problem of CaPO polyatomic interference appears to be limited to certain matrix types
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and is likely related to the density and strength of the mineral structures in different skeletal
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tissues. For archaeological teeth, a method to correct for this interference is necessary in order to 8
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obtain accurate data from samples with low concentrations of Sr (Scharlotta et al., 2011);
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however, for bone samples, there does not appear to be any significant offset in isotopic values
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resulting from this polyatomic species (Scharlotta, 2012; Scharlotta et al., 2013).
283 284
4. Materials
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4.1. Environmental samples
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As noted in many studies (e.g., Beard and Johnson, 2000; Bentley, 2006; Bentley and
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Knipper, 2005; Ezzo et al., 1997; Ezzo and Price, 2002; Haverkort et al., 2008; Hodell et al.,
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2004; Price et al., 2002; Weber et al., 2003), understanding of human geochemical signatures
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(from bones or teeth) is best achieved in the context of the biologically available geochemical
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environment. Regardless of the actual geochemical properties of the rocks and soils, all that
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laboratory analysis will show is the interaction between human consumer and the biologically
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available geochemical environment. That is, the water sources are the fundamental vector for
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both plants’ and animals’ interactions with their environment, and thus understanding of the
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water is central to the compositional signatures that will be imparted upon their human
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consumers.
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Modern surface water may not reflect compositional characteristics of the same water source
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throughout prehistory as erosion factors can alter the geological interactions or contributions to
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the water’s composition through time. Sampling of smaller water courses and geochemical
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contributions to the larger rivers as well as identifying localized geologic features can help
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address this matter. Small (e.g., Bugul’deika, Manzurka, etc.) and medium (e.g., Irkut) rivers
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provide important information for the development of regional distribution maps of geochemical
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signatures corresponding to very specific geographical areas as opposed to the averaged total of
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entire drainages represented by large rivers (e.g., Lena). Therefore, water samples were taken
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from accessible rivers (i.e., flowing at the time of sampling; Table 2, Fig. 2). Only one water
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sample was collected per each sampling location, with an effort to sample each river at consistent
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intervals (ca. 50 km). Consequently, the longer the body of water, the more samples collected for
307
analysis.
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Water, while an important interaction vector for all living animals, does not comprise the
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primary contribution to geochemical signals imparted on skeletal tissues. Biologically available
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chemical components are present in far higher concentrations in plants than in water 9
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(approximately 10 times higher for elements such as strontium). As noted, plant foods are likely
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not substantial contributors to the human diet in the Cis-Baikal region, but do, however,
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comprise the majority or entirety of the diets of animals that humans would have consumed.
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Animals eat a variety of plants that are indigestible to humans and so will have a much broader
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interactions with the geochemical environment. To track the range of potential inputs into
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animal’s diets, sampling of plants was conducted throughout the archaeological micro-regions of
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Cis-Baikal. A few plant samples were collected at each water-sampling location and additionally
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at intervals of approximately 15–20 km. If possible, species with different root structures (i.e.,
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depths) were collected in order to assess better the possible range of biologically available
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interactions in both direct consumable plant matter and in contribution towards local soil
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production.
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Haverkort et al. (2008) noted that the majority of Baikal faunal samples available at the time
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did not have exact provenience and could only be attributed to micro-regions. Each individual
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animal will have its own foraging range which may overlap entirely or not at all with the micro-
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region from which it was recovered and assumed to measure its geochemical signatures. This
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leaves the potential for disparity between the geographic location of the sampling site (i.e.,
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modern find or archaeological site) and the location of the actual foraging range (i.e.,
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geochemical interactions with the environment) the animal experienced in life. Archaeological
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faunal materials have an additional disadvantage in that the animals could have been transported
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quite long distances from hunting grounds to places of archaeological recovery. The utility of
331
large, frequently migratory, fauna – modern or archaeological – as source of reference materials
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is thus seriously compromised by the lack of information about the geographic location of the
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animal foraging range. For example, a sample of roe deer recovered from the Little Sea may
334
actually come from an animal that spent most of its life on the Upper Lena.
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Consequently, small animals or domesticates are frequently considered the preferred choice
336
for reference samples, at least for studies involving agro-pastoral groups, as they generally have
337
limited and spatially rather fixed foraging ranges that are thus more conducive to the
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understanding of the distribution of the biologically available geochemical tracers. Domesticates
339
are irrelevant to our case, as no domesticated food resources are present in the region prior to the
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Iron Age. The problem here is that small fauna usually do not contribute much to the human diet
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and there is currently no evidence to support their contribution to the diet of hunter–gatherer 10
342
groups examined here. Next, in Cis-Baikal small fauna is usually rare at archaeological sites
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(e.g., Novikov et al., 2007, 2008) and collection of modern specimens in remote locations is
344
rather impractical while troubled by anthropogenic pollution near modern human settlements .
345
Furthermore, the terrain of the Cis-Baikal region is highly variable, much of it not conducive to
346
systematic sampling of small fauna within the time frame available for fieldwork resulting in a
347
reference dataset biased towards a few pockets with more open taiga-steppe terrain and close to
348
the archaeological sites excavated over several years (e.g., Little Sea). Overall, such samples are
349
considered of limited utility and are included in the study only for general comparative purposes.
350
Since plant and water samples have fixed and confirmed provenience and reflect bioavailable
351
geochemical signatures with greater chronological stability, they also provide a more accurate
352
picture of prehistoric bioavailability of geochemical tracers than modern or archaeological fauna
353
can provide. In sum, plants and water are our materials of choice for reference purposes. Thus,
354
assessment of biologically available
355
combination of plant (n = 179) and water (n = 60) samples, and faunal bone and teeth (n = 124)
356
for a total of 363 samples (Fig. 2, Table 2). Faunal samples included those used by Haverkort et
357
al. (2008) and 51 new specimens taken from areas not sampled before (e.g., Tunka micro-
358
region). Sampling focused on archaeological micro-regions in order to examine the nature and
359
extent of movement both within and between micro-regions.
87
Sr/86Sr and elemental data was conducted using a
360 361
4.2. Human samples
362
As mentioned, the main reason this study focuses on individuals from small cemeteries is that
363
the geochemical research on Baikal materials conducted to date created a geographic and cultural
364
bias toward large cemeteries in the Angara valley (e.g., Lokomotiv and Ust’-Ida) and Little Sea
365
area (e.g., KNXIV and Kurma XI). Integration of the new human data with the information
366
already available in the context of the much larger, spatially and quantitatively, set of
367
environmental background values will provide for a more comprehensive and balanced regional
368
framework of reference data with which to assess human movement across the entire Cis-Baikal
369
region. Five small cemeteries representing two out of four main archaeological micro-regions
370
were available for analysis: Borki 1, Manzurka, and Obkhoi from the Upper Lena valley, and
371
Khotoruk, and Shamanskii Mys from the Little Sea area (Fig. 2, Table 1). No materials were
11
372
available from the Tunka area for this study as the only Neolithic–Bronze Age cemetery known
373
to date from the micro-region is Shamanka II, subject of a separate large scale examination.
374
Human samples consisted of 39 molars from the following 14 individuals: one each
375
individual from Borki 1 and Manzurka, three from Obkhoi, six from Khotoruk, and three from
376
Shamanskii Mys (Table 1, Fig. 2). In most cases three molars (M1, M2 and M3) were analyzed
377
while, due to limited availability, in three instances only two molars were examined (Table 1).
378
Since molars grow in specific, well known sequential time spans, covering the range of juvenile
379
growth from infancy to sub-adulthood (Hillson, 2002, 2005), analysis of all three molar teeth
380
from an individual provides detailed information about their movement during the full course of
381
their childhood. As each molar reflects several years of growth, every tooth was sampled at four
382
spots between the crown and the cingulum (indicated in the table with human supplementary
383
materials as samples ‘a’,‘b’, ‘c’ and ‘d’ with ‘a’ representing a spot close to crown and ‘d’
384
located close to cingulum (Fig. 3). The exact relationship between uptake, deposition and
385
mineralization of tooth enamel is still unclear, however, given the span of time reflected by each
386
tooth, four evenly spaced sampling locations should reflect perhaps one month of enamel
387
formation separated by ~six–ten month intervals between sampling locations. For example, for a
388
model individual with well-preserved molar crowns, it can be estimated that these one-month
389
long intervals (i.e., sampling lines A–D) are located within the following approximate spans of
390
age:
391
M1
Line ‘a’: birth – 6 months (i.e., 0 – 6 months)
392
Line ‘b’: 12 – 18 months (i.e., 1 year – 1 year and 6 months)
393
Line ‘c’: 24 – 30 months (i.e., 2 years – 2 years and 6 months)
394
Line ‘d’: 36 – 42 months (i.e., 3 years – 3 years and 6 months)
395
M2
Line ‘a’: 30 – 39 months (i.e., 2 years and 6 months – 3 years and 3 months)
396
Line ‘b’: 48 – 57 months (i.e., 4 years – 4 years and 9 months)
397
Line ‘c’: 66 – 75 months (i.e., 5 years and 6 months – 6 years and 3 months)
398
Line ‘d’: 84 – 90 months (i.e., 7 years – 7 years and 6 months)
399
M3
Line ‘a’: 96 – 105 months (i.e., 8 years – 8 years and 9 months)
400
Line ‘b’: 114 – 123 months (i.e., 9 years and 6 months – 10 years and 3 months)
401
Line ‘c’: 132 – 141 months (i.e., 11 years – 11 years and 9 months)
402
Line ‘d’: 150 – 168 months (i.e., 12 years and 6 months – 14 years) 12
403 404
It is important to remember, however, that these general estimates will vary in each specific case
405
due to the unique and variable condition of the tooth crown examined.
406 407
4.2.1. Upper Lena: Borki, Manzurka, and Obkhoi cemeteries
408
The three Upper Lena cemeteries were excavated in the 1970s and are known only from the
409
Russian fieldwork reports (Okladnikov, 1971) or a short preliminary publication (Konopatskii,
410
1977). While Borki and Obkhoi produced only EBA graves, three and 12 respectively, one Early
411
Neolithic (EN) and four EBA graves were found at Manzurka. With the exception of the EN
412
Manzurka burial, all other individuals examined here date to the EBA (Table 1). Although
413
carbon and nitrogen stable ratios for some of these individuals are known (Weber et al., 2002),
414
so far no strontium or trace element tests have been carried out on materials from the Upper
415
Lena.
416 417
4.2.2. Little Sea: Khotoruk and Shamanskii Mys cemeteries
418
Khotoruk is a small cemetery located near the mouth of the Anga River on the northwest
419
coast of Lake Baikal, about 50 km southwest of Ol’khon Island (Konopatskii, 1982; Mamonova
420
and Sulerzhitskii, 1989; Weber et al., 2006). Based on morphological properties of some of the
421
grave goods, the seven graves excavated between 1977 and 1979 have been typologically
422
assigned to the EN which is consistent with the available radiocarbon dates (Mamonova and
423
Sulerzhitskii, 1989; Weber et al., 2006). Importantly, the mortuary protocol documented at
424
Khotoruk is quite different from the Kitoi mortuary tradition known from the Angara valley and
425
south Baikal.
426
Shamanskii Mys, situated on the northwest coast of Ol’khon Island (Goriunova and Novikov,
427
2010; Konopatskii, 1982; Okladnikov and Konopatskii, 1974/1975;
Weber et al., 2006),
428
produced 11 graves. Based on grave architecture and accoutrements, one grave was classified as
429
EN, three as Late Neolithic (LN), and seven as EBA, which is also consistent with the available
430
radiocarbon dates (Table 1; Weber et al., 2006). Only the EBA individuals are analyzed in this
431
study.
432
Examination of these two small samples from the Little Sea area is practical for two reasons.
433
First, Khotoruk is one of very few EN cemeteries in the Little Sea area which are all invariably 13
434
small. Second, the island location of Shamanskii Mys contrasts with the mainland EBA
435
cemeteries in the Little Sea area (e.g., KNXIV), the latter being in vast majority. Thus, both
436
samples expand temporal and spatial scales of comparison between data sets. Carbon and
437
nitrogen stable isotope results are also available for most of the Shamanskii Mys individuals but
438
not for the Khotoruk burials where the postcranial skeletons were poorly preserved and
439
comingled in case of graves with multiple interments as in Grave 4 excavated in 1977 (Table 3,
440
Fig. 4; Weber et al., 2002, 2011).
441
In addition to the strontium isotope ratios and trace element data generated for the 39 teeth
442
representing 14 middle Holocene foragers from the Little Sea and Upper Lena micro-regions of
443
Cis-Baikal, we also employ carbon and nitrogen stable isotope results which were obtained for
444
nine out of the fourteen individuals concurrently with radiocarbon dating (Table 3, Fig. 4).
445
Furthermore, two additional larger sets of carbon and nitrogen stable isotope signatures for the
446
Little Sea and Upper Lena micro-regions are available. First, several dozen individuals from a
447
number of sites, including such large cemeteries as KNXIV and Kurma XI (Fig. 5), have been
448
examined previously in collaboration with Dr. M.A. Katzenberg, University of Calgary
449
(Katzenberg et al., 2009, 2012; Weber et al., 2002, 2011). Second, ca. 60 new results have been
450
obtained recently from the Oxford Radiocarbon Accelerator Unit, Oxford University, as part of
451
the comprehensive dating of middle Holocene mortuary sites at Cis-Baikal (Table 4 and Fig. 6).
452
The stable isotope results are relevant for two reasons. First, they allowed the important
453
distinction between two different diets existing among the middle Holocene foragers in the Little
454
Sea micro-region – Game-Fish-Seal (GFS) and Game-Fish (GF), a distinction to which we refer
455
many times throughout the paper. And second, together with strontium isotope data (Weber and
456
Goriunova, 2013), the carbon and nitrogen signatures led to the formulation of hypothesis about
457
the asymmetrical migration of foragers between the Little Sea and Upper Lena micro-regions
458
during the EBA, a subject of further examination in this study.
459 460
5. Sample preparation and analysis
461
Sample pretreatment varied depending on the material analyzed. For water samples, 10 mL
462
was filtered through a 0.2µ fiber filter and evaporated to dryness prior to transport in
463
polypropylene vials, rehydrated with milliQ (MQ) de-ionized water and evaporated to dryness in
14
464
Teflon beakers under a laminar flow hood. Dried plant leaves or needles were ashed in a muffle
465
furnace for 18 hours at 650 ºC prior to further processing.
466
Sample solution preparation occurred in a Class 100 clean room facility and followed
467
procedures outlined in Haverkort et al. (2008) and Scharlotta et al. (2011). Plant, water and
468
faunal samples were analyzed for elemental composition using solution mode-ICP-MS and for
469
87
470
with 1 mL of 2% HNO 3 prior to necessary dilution for MC-ICP-MS analysis. Analysis was
471
conducted on a Nu Plasma HR MC-ICP-MS with a DSN-100 nebulizer. Accuracy and
472
reproducibility of the analytical protocol based on long-term repeated analysis of a 100 ppb
473
solution of the NIST SRM 987 strontium isotope standard 0.710242 ± 0.000041.
Sr/86Sr ratios using multi collector-ICP-MS. Dried strontium isotopic samples were dissolved
474
Elemental samples underwent similar handling, though not loaded onto cation exchange
475
columns. Digested samples were dissolved in 2% HNO 3 prior to ICP-MS analysis. Sample
476
solutions were analyzed for 49 elements (Li, Na, Mg, Al, P, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu,
477
Zn, Ga, As, Rb, Sr, Y, Zr, Nb, Mo, Ag, Cd, Sn, Sb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy,
478
Ho, Er, Tm, Yb, Lu, Ta, Au, Tl, Pb, Th, and U) using a Perken Elmer Elan6000 quadrupole ICP-
479
MS. External reproducibility, based on repeated analysis of international whole rock standards is
480
5–10% (2σ level) for most elements (Supplementary Table S1).
481
Laser ablation for elemental analysis of human tooth samples was conducted using the Perken
482
Elmer Elan6000 quadrupole ICP-MS coupled to a UP213 nm laser ablation system (New Wave
483
Research, USA). Quantitative results for the same 49 elements analyzed in solution, were
484
obtained and normalized to
485
Macquarie University) laser ablation software.
24
Mg, as the internal standard using the GLITTER® (XP version,
486
Laser ablation for isotopic analysis of human teeth was conducted using a UP213 nm laser
487
system coupled to the Nu Plasma HR MC-ICP-MS with the sample-out line from the desolvating
488
nebulizing introduction system (DSN-100 from Nu Instruments) to allow for simultaneous
489
aspiration of a 2% HNO 3 solution. At the beginning of each analytical session, parameters for
490
the introduction system and the ion optics were optimized by aspirating a 100 ppb solution of the
491
NIST SRM 987 Sr isotope standard. Machine drift was monitored using a Durango Apatite
492
reference material (Supplementary Table S2). All laboratory work was conducted at the
493
Radiogenic Isotope Facility of the Department of Earth and Atmospheric Sciences at the
494
University of Alberta, Edmonton. 15
495
A combination of statistical techniques developed specifically for the unique needs of
496
chemical sourcing were employed to analyze the resulting geochemical data, following
497
established approaches described by Baxter (1994, 1994), Baxter and Buck (2000), Davis (1986),
498
Glascock (1992), Glascock et al. (1994), Hoard et al. (1992), and Truncer et al. (1998). Analyses
499
of regional variance and means were conducted. Following this, in order to explore the data
500
further and assess to what extent cultural micro-regions could be separated in multivariate space,
501
additional exploration using bivariate plots, principal component and canonical discriminant
502
analyses were conducted on a combination of 87Sr/86Sr isotopic and elemental concentration data.
503 504
6. Results
505
6.1. Environmental samples Preliminary examination of
506
87
Sr/86Sr variability in faunal material associated with
507
archaeological micro-regions by Haverkort et al. (2010; 2008) demonstrated promising results.
508
As the sample size was expanded to include more animal specimens, water and plant samples,
509
the range of
510
variance and means successfully exhibited differences; however, the extent of overlap between
511
micro-regions rendered moot effective correlations between geological, geochemical and
512
archaeological areas as previously described. Rather than directly reflecting bedrock geological
513
formations with some attenuation for translating bedrock structures into biologically available Sr,
514
a confounding overlap in the ranges of
515
Furthermore, even groups of similarly aged geologic formations (i.e., Archean and Proterozoic
516
areas along the western coast of Baikal and in the Tunka micro-region) exhibited different
517
87
518
featured distinctly higher
519
micro-region apparent. Previous work (e.g., Haverkort et al., 2008) indicated a spike in both
520
terrestrial and aquatic faunal
521
adjacent to the KNXIV cemetery; however, this effect appears now to be more widespread
522
through the micro-region (Figs. 1 and 8). Fig. 8 provides a visual approximation of the expected
523
bioavailable isotopic values throughout the major archaeological micro-regions based upon
524
analysis of environmental samples and expanded to dominant topographic features of the
525
landscape. The great variation in strontium isotopic values observed in water, plant, and animal
87
Sr/86Sr variability proved to be quite large (Table 6). Analysis of micro-regional
87
Sr/86Sr values was found through all micro-regions.
Sr/86Sr values (Fig. 1). The clear exception to these overlaps is the Little Sea area which 87
Sr/86Sr values making any contact with bioavailable Sr from this 87
Sr/86Sr values associated specifically with the Sarma drainage
16
526
samples highlights the important distinction between the dominant geological formations as the
527
source of bioavailable radiogenic materials and the actual bioavailable isotopic values in the
528
environment (cf. Sillen et al., 1998).
529
Analysis of water provides insight into the extent and boundaries of some of the broader As noted, water is a fundamental vector for plant and animal
530
biogeochemical processes.
531
interactions with their environment. The overall impact of direct water consumption, however, is
532
limited due to the low concentrations of elements within the water. Bentley (2006: Table I)
533
notes that concentrations of Sr in water ranges from 0.001 to 0.8 parts per million for fresh water
534
sources. In contrast, edible plants contain 1 to 100 ppm and mammal bones contain 100 to over
535
1000 ppm (see discussion below). Thus, vastly greater quantities of water must be consumed in
536
order to match the chemical contributions of either plants or animals in the diet.
537
Examining a single watercourse over some distance has produced rather surprising variability
538
in Sr ratios (Table 7). For example, the Irkut River was sampled repeatedly from its origin at a
539
small lake in the Sayan Mountains. Near the origin, the river is a small stream, to be joined by a
540
number of very small drainages and combining with the White Irkut (2009.280) and Black Irkut
541
(Samples 2009.265, 2009.273). Near the Mongolian border the river becomes known as the
542
Irkut River (Sample 2009.284).
543
approximately 30 km, the water’s Sr ratios vary from 0.71373 at the source, down to 0.70711
544
shortly before the spot where the river changes its name, and back up to 0.70878 once the river is
545
identified solely as the Irkut. Given the relatively small amount of water in the river and
546
generally high degree of chemical weathering in this section, contributions from small inlets can
547
have a fairly large impact on the overall chemical composition. Through the Tunka Valley, the
548
Sr ratios are quite stable in comparison, ranging from 0.70862 to 0.70886, with a jump to
549
0.70927 (Sample 2009.216) shortly after leaving the valley. Between the Tunka Valley and the
550
Angara drainage, values are again similar, ranging from 0.70927 to 0.70982. The size of the
551
river is greater along these sections and contains a greater sediment load, thus the overall
552
chemical composition will reflect the river as a whole along with soil-water contact values as
553
opposed to rock-water contact. All sampling was conducted during the summer, so no data are
554
available to determine if seasonal variability in sediment load within rivers will significantly
555
alter the biogeochemical values experienced by humans and animals in contact with a given river
556
or stream.
Even in this early section of the drainage, spanning
17
557
Another interesting difference was documented within the Little Sea. Four samples, of what
558
is all effectively water from Lake Baikal (Samples 2009.436, 2009.443, 2009.450, and 2009.454)
559
were collected along the southern margin of the Little Sea, yet produced markedly different Sr
560
values. The first was taken from the northwestern coast of Ol’khon Island, near the Shamanskii
561
Mys cemetery, producing Sr values of 0.70922. The second was taken in an area called Zolotye
562
Vorota, or the Golden Gates, in between the northwest coast of the lake and the southern end of
563
Ol’khon Island, connecting the southern end of the Little Sea to the main portion of the lake,
564
produced similar results at 0.70902. These are both similar to the 0.70895 value observed at
565
Listvianka, at the source of the Angara River. These three values were obtained for samples
566
collected either from locations close to deeper waters (Shamanskii Mys and Zolotye Vorota) or
567
directly on the open lake coast (Listvianka) and produced values that are similar although a little
568
higher than the 0.7085 result reported by Kenison Falkner et al. (1992). Such variation in such a
569
large body of water with numerous chemically different sources of input is not surprising.
570
What is surprising, however, is the remaining two values from the middle portion of the Little
571
Sea, both collected from shallow coves. One sample (2009.450) taken near the Kurma cemetery
572
produced a value of 0.72512, while a second (2009.454) from near the Khuzhir-Nuge XIV
573
cemetery was higher still at 0.73830 (Table 7). Extremely high Sr isotope ratios were observed
574
in fish, plants, and water samples from the adjacent areas as well. The overall impact on the lake
575
itself is surprising given the relatively small volume of water flowing in local streams. The
576
0.72512 value was obtained for a sample taken from a flooded marshy area where a stream flows
577
into the lake, so it is not immediately clear if this represents primarily the outflow of the stream,
578
or a combination of high values such as those seen further south along the western coast mixed
579
with the larger body of lake water. The extent of mixing between this area of partially separated
580
and calm water likely varies throughout the year based on stream outflow and water movement
581
within the Little Sea as a whole. The incomplete mixing of waters from different parts of the
582
lake depending on bathymetry and coastline topography is also important for researchers
583
calculating of radiocarbon offsets based on freshwater reservoir effects.
584
In contrast, the nearby Upper Lena micro-regional water values are quite homogeneous. The
585
Lena River itself was sampled three times, producing values from 0.70865 to 0.70893, very
586
similar to the 0.709 value reported by Huh et al. (1994) for the upper Lena watershed as a whole.
587
Our mean value for the Upper Lena micro-region was 0.70856, reflecting some degree of 18
588
geographical variation in strontium ratios, but also showing a standard deviation that is orders of
589
magnitude smaller than in the other micro-regions. Very short distance movements in the Little
590
Sea can drastically alter the biogechemical environment, while fairly extensive movements could
591
be conducted in the Upper Lena, or indeed the Central Siberian Plateau, with very little
592
difference in terms of Sr isotopic ratios.
593
Based on the data obtained for smaller streams as well as rivers together, the Tunka micro-
594
region looks markedly less homogeneous. The Irkut River is fairly stable in terms of Sr ratios, at
595
least in its middle and lower sections, but smaller watercourses range from 0.70711 to 0.071373.
596
A similar pattern of isotopic variation, although smaller in range, is seen in the Angara Valley,
597
with values ranging from 0.70791 to 0.71169. The extent of this range of isotopic variability and
598
degree of overlap between these areas makes them indistinguishable as geochemical groups
599
based solely on water values. These situations highlight the importance of using Sr isotope ratios
600
in tandem with either another isotopic series, or, as in this case, trace elemental analysis.
601
Due to the complexity of the situation, multivariate statistical approaches were employed to
602
enable geochemical group discrimination. Samples of water, plants, and faunal bones collected
603
displayed varied elemental concentrations on orders of magnitude of difference. Data were
604
converted to ratios relative to internal Mg concentrations in order to make the different sample
605
types comparable. Ratios to all available elements were attempted, but none provided a more
606
useful marriage of different sample types (water, plants, and human or animal skeletal materials)
607
than using Mg as the internal standard. Faunal materials were excluded from the analysis at this
608
point as the nature of the provenience information, as discussed above, frequently made
609
individual archaeological micro-regional group or arbitrary geochemical sub-group membership
610
uncertain. Remaining environmental comparisons were made on all plant and water samples.
611
Principal component analysis (Fig. 9) and discriminant function analysis (Fig. 10) were
612
attempted on geochemical micro-regional groups reflecting the Upper Angara, Tunka, Baikal
613
coast/Little Sea, and the Upper Lena micro-regions.
614
Following this, micro-regional groups were subdivided into two–four subgroups each based
615
on combinations of 87Sr/86Sr and elemental values for plant and water samples resulting in a total
616
of 11 such subgroups (Fig. 11). These multivariate data are presented only as the 2 sigma
617
confidence ellipses to improve visibility (Figs. 9–14). Unfortunately, 87Sr/86Sr ratios presented
19
618
the dominant vector of variability in these subgroups and the complexity in multivariate space
619
did not support effective micro-regional provenancing of individuals.
620
Given the observed geochemical complexity, a different approach was taken to discriminate
621
the geological micro-regional groups. The entire data set of strontium isotopic ratios and trace
622
element results for plant and water samples was broken down into subranges reflecting limited
623
87
624
Baikal 87Sr/86Sr values was divided into eight subranges (0.720), and then for each of these geochemical subranges, the
626
elemental data were compared between micro-regions. Geochemical subranges do not correlate
627
to specific geological characteristics, reflecting similarities present only in statistical space. This
628
effectively moderates the dominant effect of the
629
87
630
subsequently micro-regions are compared primarily based on elemental data. Conducted in this
631
fashion, comparison between geochemical subranges reveals rather distinct micro-regional
632
differences (Fig. 12).
Sr/86Sr variability and then re-divided by micro-region. In this case, the whole range of Cis87
Sr/86Sr isotopic and
87
Sr/86Sr values within multivariate space.
Sr/86Sr values are still crucial for accurate classification within appropriate subranges, but
633 634
6.2. Human samples
635
Human results were transformed using the same discriminant function matrix as plant and
636
water data and compared to established micro-regional subranges to establish their provenance
637
(Table 8). Strontium isotope ratios were directly comparable across the different reference
638
sample types, but trace elemental data had to be converted prior to comparisons. With disparate
639
comparative materials, archaeological micro-regional subrange group classifications were
640
difficult and could not be verified by techniques such as Mahalanobis distance measurements.
641
Instead, human samples were tested for their hypothesized subrange group membership by a
642
visual version of jackknifing. Samples added to a micro-regional subrange group would either
643
cluster closely with the group, or stretch the group in the direction of the appropriate group
644
membership. All samples were tested as members of all available groups before provenance
645
determination and final group membership were established. Once classified by micro-regional
646
subrange, samples were compared both visually to the reference samples in multivariate biplots
647
(e.g., Figs. 13 and 14) and through hierarchical cluster analysis to verify the geographic sampling
648
site with the greatest affinity to the human specimen analyzed. 20
649
As already mentioned, previous geochemical work in the Cis-Baikal region supported the
650
feasibility of mobility research and suggested significant patterning of the 87Sr/86Sr values likely
651
indicating different mobility or migration patterns. With an expanded environmental reference
652
dataset, better understanding of the geological complexity in the region has come to light. As
653
such, it is difficult to input the results for individuals examined in this study directly into the
654
interpretive framework presented previously by Haverkort et al. (2008) and Weber and
655
Goriunova (2013). Comprehensive discussion of this framework in light of new knowledge
656
about
657
paper. Instead, qualitative discussion of the 14 individuals analyzed in this study will be provided
658
in the context of new data. As noted, samples were first analyzed for Sr ratios in order to
659
determine the appropriate subrange for trace elemental comparison. Trace elemental analysis
660
consisted of transforming the raw compositional data into discriminant function values in
661
multivariate statistical space, and projecting them against environmental comparison groups
662
formed from sampling water, plants, and faunal materials.
87
Sr/86Sr ratio variability throughout the Cis-Baikal region is beyond the scope of this
663
Given this approach, samples can either be presented as statistical distance classifications
664
from individual points, or projected, for example, on a bivariate plot to assess visually the closest
665
environmental reference point. Once this is determined, the geographic location of that reference
666
point can be used to infer the area of interaction to some degree. In some cases, human samples
667
plotted very closely to individual or clustered groups of points from a very specific area. In other
668
cases, the human data indicate group membership for a specific micro-region, but do not fall near
669
any of the reference points. This was generally interpreted as reflecting the limitations of the
670
current reference collection. Future environmental sampling may fill in some of the current gaps
671
and permit a re-analysis of human results in order to refine the provenance determinations.
672
Table 8 contains provenance information for each examined molar as inferred on the basis of the
673
line of reasoning presented above.
674 675
6.2.1. Borki 1
676
The one EBA individual (BO1_1971.001) from Borki displayed results for three molars that
677
were fairly tightly clustered suggesting spatially limited mobility as a subadult. Sr ratios for all
678
sampling lines ranged from 0.70886 to 0.71183 (Supplementary Table S2). These values are
679
fairly low and stable, alone suggesting the relatively homogenous Upper Lena micro-region. 21
680
The upper portion of this range suggests some contact with areas outside of the Upper Lena
681
micro-region, or contact focused in areas of the Upper Lena with higher Sr isotope ratios. Trace
682
elemental data were then compared with environmental samples in the 0.708–0.710 and 0.710–
683
0.712 subranges. In both subrange comparisons, provenance data showed equal affinity to both
684
the Upper Lena and the Little Sea micro- regions, likely indicating fairly stable residence in or
685
near the mountains of the Primorskii Range upstream along the Lena River. This individual was
686
interred near Kachug, an area well represented by the environmental data set, yet apparently did
687
not enter it until adulthood, a phase in life not documented by geochemical tracers recovered
688
from tooth enamel.
689 690
6.2.2. Manzurka
691
The geochemical results for the EN individual (MNZ_1974.002) from Manzurka suggest that
692
this person spent significant portions of childhood in the area of the Manzurka and Khodontsa
693
Valleys which is in very close proximity to its final resting place. Sr isotope ratios are low,
694
ranging from 0.70817 to 0.70884 (Supplementary Table S2), suggesting residence in the Upper
695
Lena region. This is one of the few individuals who could be easily classified as “local”, i.e.,
696
buried in the same area where they were born and spent much of their subadulthood. Data from
697
the later portions of M1 and much of M2 suggest interactions with portions of the Upper Lena
698
not sampled yet. The overall geochemical composition remains the same and provides no
699
indication for micro-regional scale movements, but also does not hold strong correlations with
700
any areas within the micro-region that have been sampled thus far. Given the limited number of
701
sample points, it is therefore likely that these data indicate an un-sampled area within the Upper
702
Lena micro-region.
703 704
6.2.3. Obkhoi
705
Three individuals from the Obkhoi cemetery were analyzed, showing similar patterns of
706
mobility in two individuals, and an apparent lack of mobility in the third. The first individual
707
(OBK_1971.005) stayed firmly within the Upper Lena micro-region, was born near the
708
Manzurka Valley, moved to the Khodontsa Valley by the age of ~five years, moved next to areas
709
not covered by the current map, and finally returned to the vicinity of Obkhoi as an adult or after
710
death. Their Sr ratios ranged from 0.70801 to 0.71119 (Supplementary Table S2), well within the 22
711
Upper Lena micro-regional values and subrange. Strontium ratios were relatively stable for M1
712
and the first half of M2, ranging from 0.70950 to 0.70992 with the fourth (D) and fifth (E) points
713
on M1 showing 0.70810. These outlier points from around the age of three may indicate an
714
earlier movement. The third (C, 0.71053) and fourth (D, 0.71080) points on M2 show that Sr
715
ratios are rising. The delay in averaging body water strontium values and mineralization would
716
suggest that a movement had occurred before M2 was halfway developed, or approximately at
717
the age of five. Trace elemental analysis indicated a close affinity of M1 for samples from the
718
Manzurka Valley. M2 values, however, were closely related to samples from the Khodontsa
719
Valley. If an intermediate or additional movement occurred between areas near Manzurka and
720
Khodontsa, as suggested by Sr ratios, it is not clear based on the current trace elemental data. By
721
M3, the individual no longer showed strong similarities to any sampling location, but still falls
722
within the Upper Lena area.
723
The second individual (OBK_1971.013) lived their life in areas in the vicinity of the
724
confluence of the Kulenga and Lena Rivers (Kachug), however, also appears to have lived in
725
areas of the Upper Lena not sampled in this study. Sr ratios range from 0.70946 to 0.71438
726
(Supplementary Table S2), requiring comparison with three separate strontium subranges. Some
727
M1 values are higher than expected for the Upper Lena micro-region, yet trace elemental
728
analysis indicated strong similarities with the area near the Kulenga and Lena rivers. Many of
729
the points do not correlate strongly with any reference sample. Sr ratios change frequently with
730
this individual, suggesting numerous movements between areas with higher and lower Sr isotope
731
ratios. This frequent movement could produce averaged trace elemental values in the body that
732
do not fit well with reference samples indicating discrete locations.
733
While these two individuals suggest fairly significant mobility within the Upper Lena micro-
734
region but not much outside of it, the third individual (OBK_1971.007) lived most of their sub-
735
adult life in the Upper Lena micro-region, likely in proximity to the Manzurka Valley. The two
736
molars available for this individual are an M1 and M2, with Sr ratios range from 0.70933 to
737
0.71170 (Supplementary Table S2). This individual was different from the other individuals
738
from Obkhoi in that its geochemical signatures displayed clear influence coming from the Little
739
Sea micro-region slightly skewing results prior to moving finally closer to the Little Sea near the
740
end of both M1 and M2 mineralization that is at the age of around 7–8, and 10–12 years old. For
741
several of the Sr subranges, the trace elemental composition of the Little Sea and Tunka micro23
742
regions was quite similar, a likely result of limited sample size within the subrange. Additional
743
steps of chemical comparison can be conducted to separate out group proximity or membership
744
in cases where overlaps need to be clearly discriminated out. However, in cases such as this
745
particular individual from Obkoi, such similarity is useful in highlighting a measure of variability
746
within the micro-region. The likelihood of an ambiguous movement between the Upper Lena
747
and the Tunka micro-regions is far lower than that of a movement between the neighboring Little
748
Sea and Upper Lena micro-regions. This individual was clearly in contact with both Upper Lena
749
and Little Sea micro-regions.
750 751
6.2.4. Khotoruk
752
Six EN individuals from Khotoruk were analyzed and all appear to have lived their sub-
753
and young adult lives within either the Little Sea micro-region or within the northwest coast of
754
Baikal. More specifically, two individuals (KHO_1978.004.02 and KHO_1978.004.04) appear to
755
have lived on the southern and central shores of the Little Sea, staying within fairly high 87Sr/86Sr
756
ratio zones indicative of the granitic rocks of Sarma Canyon and surrounding areas (Fig. 8).
757
KHO_1978.004.02 had overall Sr ratios ranging from 0.72474 to 0.72991, while
758
KHO_1978.004.04 ranged from 0.72104 to 0.72908 (Supplementary Table S2). Such high
759
values were only noted within the Little Sea micro-region. Subrange comparison between
760
micro-regions was not necessary, as the Little Sea was the only possible micro-region and only
761
one area within this micro-region exhibited such high Sr values.
762
Two other individuals (KHO_1977.002 and KHO_1978.004.03) were likely born and spent
763
their early childhood in similarly high 87Sr/86Sr ratio zones, with Sr ratios of 0.71781 to 0.72722
764
and 0.71714 to 0.72682, respectively (Supplementary Table S2).
765
highest Sr values (0.72337 to 0.72722) on M1, with values decreasing to 0.717–0.720 on M2 and
766
M3. Interestingly, the values on M2 and M3 oscillate between higher and lower 87Sr/86Sr ratio
767
values at each sampling point. This pattern begins on M2 below the crown, so likely reflects an
768
approximate age of five years old when this individual left the very high Sr areas adjacent to the
769
Little Sea and began making repeated movements between higher and lower Sr areas.
770
KHO_1978.004.03 was analyzed only for M1 and M2 values which showed a similar pattern.
771
M1
772
0.71782. The M2 values indicate that once the individual left the very high Sr area, they went to
87
KHO_1977.002 had the
Sr/86Sr ratios range from 0.72166 to 0.72682 while M2 values range from 0.71714 to
24
773
an area with relatively homogenous Sr signatures. There are multiple areas with these Sr values
774
and subrange comparison did not highlight a likely area within the Little Sea micro-region. As
775
such, at this point it is impossible to identify the new area included in this person’s foraging
776
range. It is likely that the shift in
777
different diet for children and their mothers, though this remains speculative given the narrow
778
dietary choices available in the region.
87
Sr/86Sr ratios indicates the effects of weaning foods and a
Two individuals (KHO_1978.004.01 and KHO_1978.007) displayed some variability in their
779
Sr/86Sr ratios
geochemical signatures (Supplementary Table S2).
781
ranging from 0.71104 to 0.71460. M1 and the first half of M2 values show a slow oscillation
782
from 0.71378 to 0.71332 then back up to 0.71458. This pattern is repeated across M2 and M3,
783
though the periodicity becomes more frequent, with alternating high and low Sr values at each
784
sampling point. KHO_1978.007 has 87Sr/86Sr ratios ranging from 0.71196 to 0.71556. Their M1
785
values were stable at around 0.712, but jump to between 0.713 and 0.715 on M2. M3 then sees a
786
return to values around 0.712. This individual demonstrates that a potential time gap in between
787
individual teeth should be taken into account when viewing sequences of micro-sampled data.
788
For example, the movement event may have been occurred immediately preceding or during
789
primary crown formation, allowing ample time for body water averaging effects before evidence
790
of the movement appears on the next molar.
791
comparisons indicate no likely areas within the Little Sea micro-region, suggesting that they
792
lived in areas that were not well represented in the reference material, perhaps in an area of the
793
northwest coast of Baikal. Lower
794
trace element signatures suggest the area between the Kuda Valley and the northwestern shores
795
of Lake Baikal south of the Little Sea, as likely candidates for their provenance.
87
KHO_1978.004.01 had
87
780
For both of these individuals, subrange
Sr/86Sr ratios (e.g., 0.712 rather than 0.720+; Fig. 8) and
796 797
6.2.5. Shamanskii Mys
798
Three individuals from the Shamanskii Mys cemetery were analyzed and, though all date to
799
the EBA, each exhibited a different mobility history. One individual (SHM_1972.002), a 36–
800
50/55 years old male, showed relatively minor variations in their molar geochemical data. Their
801
87
802
Supplementary Table S2) was above 0.714, and two points between 0.713 and 0.714. Remaining
803
points were between 0.712 and 0.713, very low for the Little Sea coast (Fig. 8). The subrange
Sr/86Sr ratios range from 0.71217 to 0.71559, though only a single point on M2 (2000.206d,
25
804
comparison suggested that this person most likely lived their entire sub-adult life on Ol’khon
805
Island and its vicinity. It is also possible that other portions of the Little Sea were used as well,
806
but with limited contact with the southern end of Ol’khon Island, the western coast of the Little
807
Sea, or the Sarma River. The elevated points may indicate limited contact with these high Sr
808
ratio areas that has been moderated by the relatively low Sr ratios contributing to the majority of
809
the individual’s diet. Light stable isotope tests suggest that this person subsisted on a diet
810
characterized also as local, comprising the Baikal seal and shallow water fishes, and terrestrial
811
game (GFS diet, Weber et al., 2011).
812
Another individual (SHM_1973.003.01), a 20+ years old probable female, only had M2 and 87
Sr/86Sr ratios clustered between 0.71112 and 0.71250,
813
M3 available for analysis, but showed
814
though most values fall between 0.71164 and 0.71185 (Supplementary Table S2). Subrange
815
comparison shows this individual to have grown up on the Upper Lena, in an area not heavily
816
sampled for this study.
817
represented, there is a trend of increasing 87Sr/86Sr ratios through time. This would suggest that
818
the individual was progressively moving towards the high Sr areas of the Little Sea as a subadult.
819
Their burial at Shamankii Mys would indicate that the individual remained in the Little Sea area
820
until they died, if this progressive trend was continued. It is possible that additional movements
821
were undertaken during life, but would represent a sudden change in patterns of mobility as an
822
adult.
823
Also intriguing, is that while the early childhood years are not
The third individual from Shamanskii Mys (SHM_1975.001), a 20–30/35 year old male, 87
Sr/86Sr ratios vary from 0.70994 to
824
showed yet a different pattern of geochemical variability.
825
0.71732 with numerous fluctuations (Supplementary Table S2). The majority of M1 showed low
826
Sr values, around 0.711, jumping to over 0.7135 near the base of the tooth. This would suggest a
827
major movement around the age of two to two-and-one-half years, prior to the complete
828
mineralization of M1. M2 values begin by climbing even further, up to 0.71467, then begin
829
dropping back down, reaching 0.71084, before spiking back up over 0.71732. Between this
830
point and the first sampling location on M3, the values drop back down to 0.70994. The
831
remainder of M3 is relatively stable remaining between 0.71295 and 0.71171. Provenance
832
determination of birth locale was to an area somewhere between the Little Sea and the Upper
833
Lena. The strongest affinity is for the area around Zhigalovo, but with much time spent in areas
834
not sampled in this study. Around the age of seven–eight years this person was in some contact 26
87
Sr/86Sr ratio zones (above 0.717) that are only found near the Little Sea,
835
with relatively high
836
and around the age of 9–10 years equally in contact with areas characterized by relatively low
837
87
838
result of frequent travel between the Upper Lena and Little Sea regions yielding averaging
839
effects.
Sr/86Sr ratios (0.709). It is possible that the lack of clear provenance for this individual is the
840
Results for Shamanskii Mys suggest childhood locales in the Little Sea micro-region and
841
elsewhere, likely in the Upper Lena area. The mix of Little Sea and other micro-regional
842
geochemical signatures is intriguing evidence for the interment of individuals with varied place
843
of birth and early childhood backgrounds in a single cemetery, a pattern already identified by
844
Weber and Goriunova (2013) based on a combination of carbon and nitrogen stable isotope
845
ratios and strontium data from KN XIV – the largest EBA cemetery in the micro-region.
846 847
7. Discussion
848
7.1. Bone consumption by humans
849
Bones are not themselves a food source, but human biogeochemical contact with bones can
850
vary significantly depending on species procured and preparation methods employed. Outside of
851
well-documented bone cominution or grease rendering activities, bones, and often blood,
852
generally are not a major part of the human diet (e.g., Brink, 1997). As bone seeking elements,
853
the concentrations of elements in the bones are many orders of magnitude higher than in the
854
tissues generally sought for food, thus if any bone is ingested, it will significantly bias the
855
resulting concentrations. Small variations (e.g., 5–10%) in the dietary contributions of terrestrial
856
or aquatic animals, or of individual animals (e.g., red deer versus moose) could significantly alter
857
or bias the final elemental concentrations and the associated isotopic ratios and thus the results of
858
the analysis. Dietary and mobility analyses use similar chemical processes to address different
859
aspects of the interaction between organisms and their chemical environment. However, specific
860
discussion of the extent of potential dietary biasing of mobility data, or more broadly,
861
correlations between dietary and mobility data have been conspicuously absent in the literature.
862
Cooking weakens the mineral structure of the bone and will prompt increased release of bone
863
minerals. Any significant amount of bone stripped or cut off in meat processing could bias the
864
results of dietary analysis relative to the expected contributions. Fish bones and bone marrow
865
processing and grease soup production are likely the greatest sources of potential bias in this 27
866
respect as the most likely means by which bones will be cooked or ingested with the animal
867
tissues. Other subarctic and arctic groups with a heavy dependence on fish do not always
868
remove the bones before smoking or salting and storing the fish. A prime example of this is a
869
study of pottery residues showing the practice of cooking fish whole, including scales and bones
870
(Koch, 1998), which is similar to a Norse practice of preserving cod (a large-boned fish) with the
871
bones and then boiling the entire preserved fish into a porridge.
872
Fish generally are hard to fully de-bone without wasting a significant amount of edible tissue.
873
With increasing cooking temperature (beginning at 60 C), the heat quickly breaks down the bone
874
structure, essentially gelatinizing the entire bone (Richter, 1986). So, it may be reasonable to
875
expect a significant amount of bones to have been directly consumed by any given individual,
876
particularly given the ubiquitous nature of fish consumption amongst Cis-Baikal inhabitants
877
(Weber et al., 2011). This will undoubtedly bias the strontium, copper and lead signals towards
878
the aquatic environment from which the fish came.
879
It is realistic, however, to assume that all individuals in a group will be equally exposed to
880
such biasing factors. If we assume that all aquatic foods stemmed from the same source or that
881
groups were not imbibing foods from different aquatic micro-regions, then there is no reason for
882
concern. The difficulty arises if hypotheses regarding seasonal movements are substantiated by
883
mobility data. Then the question of what aquatic resources are consumed, and where are they
884
from becomes very important.
885 886
7.2. Carbon and nitrogen stable isotope results
887
Before we enter assessment of the new provenance results (i.e., based on strontium ratios and
888
trace element concentrations) for the 14 examined individuals in a broader context, it is useful to
889
discuss in some detail the carbon and nitrogen stable isotope data available for these persons
890
(Tables 3–5, Figs. 4–7). As mentioned, it was the combined analysis of the strontium ratios and
891
carbon and nitrogen signatures that identified the unique travel pattern between the Little Sea and
892
Upper Lena micro-regions (Weber and Goriunova, 2013; Weber et al., 2011). Two different
893
kinds of diets were found among the Little Sea foragers: GFS and GF, accounting roughly for
894
2/3 and 1/3 of the examined individuals, respectively (Fig. 5). While these two diets were found
895
within all culture historical periods examined (EN, LN and EBA), the EBA sample, the largest of
896
the three, revealed an additional pattern. Of the 24 KNXIV individuals tested also for strontium 28
897
isotope ratios, 11 were assessed to be of the local Little Sea birth and 13 of non-local birth. Alas,
898
the geochemical evidence at the time of the study was insufficient to identify the non-local place
899
of birth of these people more accurately. Therefore, the place of birth of these individuals was
900
left open and simply referred to as non-local to imply an area other than Little Sea. Interestingly,
901
this group of 13 non-locals displayed a mix of diets: six with GFS and seven with GF while the
902
group of 11 locals showed exclusively the GFS diet.
903
The carbon and nitrogen stable isotope data considered alone displayed one different pattern:
904
the distribution of Little Sea individuals with GF diet very closely overlapped the distribution of
905
the results from the Upper Lena micro-region (Weber et al., 2011, Fig. 5). This overlap was not
906
discussed in detail partly because it was corrupted to a certain extent by the EN sample from the
907
Turuka cemetery. Turuka, however, is located near Ust’-Kut on the far northern periphery of the
908
Upper Lena micro-region and geographically does not belong to the cluster of sites situated on
909
the Lena around Kachug and Zhigalovo (Fig. 2). Once the Turuka individuals are removed from
910
analysis the two distributions are essentially indistinguishable (Fig. 5, Table 5).
911
The new results, namely strontium ratios and trace element concentrations, environmental and
912
human, as well as the recent carbon and nitrogen stable isotope data from Oxford, both add new
913
dimensions and augment the previous findings. In light of the environmental results presented
914
here we now feel quite confident to identify the Upper Lena micro-region as the place of birth of
915
all KNXIV non-locals with M1 strontium isotope ratios around 0.710. Importantly, the Oxford
916
data, in combination with the results already available from Calgary, support the notion that the
917
GF diet from the Little Sea is indistinguishable from the diet of Upper Lena foragers (Figs. 5–6,
918
Table 5).
919
Furthermore, looking at the Little Sea’s GFS and GF distributions it seems that they represent
920
two discrete dietary regimes. Even more interesting is that these distributions do not show much
921
evidence for gradual transition from the GF diet of the newcomers from the Upper Lena to the
922
GFS diet. The lack of such gradual transition is quite visible in the carbon and nitrogen
923
signatures of the five non-local individuals from the KNXIV cemetery showing the GFS diet:
924
they not only belong to the lower part of the GFS diet distribution range on the δ15N axis but also
925
show the wider variation along the δ13C scale relative to the individuals with GF diet. It is
926
important to note that the greater variation along the δ13C axis is the product of variation present
927
in the aquatic foods available on Lake Baikal (i.e., seal with δ13C values between –23‰ and – 29
928
21‰ , and shallow water fishes with δ13C values between –17 and –12‰) but absent in the
929
fishes of the Upper Lena micro-region (–27‰ to –24‰). The sixth non-local person with GFS
930
diet shows a different, much higher M1 Sr isotope ratio (0.721) as well as higher δ15N signature
931
than the other five non-locals (Fig. 7). This person, thus, could not be born on the Upper Lena,
932
like the other non-locals, including those with the GF diet, but probably somewhere along the
933
northwest coast of Lake Baikal where the Sr ratios are even higher than in the little Sea area.
934
Carbon and nitrogen stable isotope results are also available for nine out of fourteen
935
individuals examined in this study and they too provide useful insights; unfortunately, resolution
936
of the sex and age data is rather low which limits analysis (Table 3, Fig. 4). All individuals from
937
the Upper Lena form a tight cluster within the Upper Lena and GF diet distributions (Figs. 4–6)
938
and they show no evidence of aquatic foods with more variable δ13C available on the Little Sea
939
being included in their diet. Only one of these individuals (OBK_1971.007) displayed Sr and
940
trace element signatures indicative of some contact with the Little Sea area during their sub-
941
adults year. However, during the long interval of ca. 15–20 years prior to their death near Obkhoi
942
the diet of this person was firmly of the local Upper Lena character, i.e., lacking any intake of the
943
Little Sea’s aquatic foods.
944
The diets of the four individuals from the Little Sea are more variable: the two EN Khotoruk
945
persons show the GF diet while the two EBA Shamanskii Mys individuals display the GFS diet.
946
The EN sample from Khotoruk and from the entire Little Sea and Upper Lena, alas, is too small
947
to make any further inferences at this point. The two EBA Shamanskii Mys individuals,
948
however, do provide additional information. Although both display the GFS diet, as do all other
949
individuals from this cemetery including those dating to EN and LN, one of them
950
(SHM_1972.002) appears to have spent all their childhood in the Little Sea area while the other
951
one (SHM_1973.003.01) appears to have been born and spent their sub-adult years on the Upper
952
Lena. Thus, SHM_1973.003.01 shows a pattern similar to a number of EBA non-locals with
953
GFS diet from the KNXIV cemetery. Unfortunately, we have no dietary data for the third person
954
examined from Shamanskii Mys (SHM_1975.001), however, if their diet was also GFS, this
955
person’s childhood likely involved relatively frequent travel between the Little Sea and Upper
956
Lena before settling down in the Little Sea and subsisting on the local diet (GFS) for quite a long
957
time prior to death (Table 3 and 4; Weber et al., 2011).
958 30
959
7.3. Broader archaeological and geographic context
960
The new strontium ratios and trace element results thus lead to a number of interesting
961
inferences. First, there is clearly a high level of agreement between the insights on mobility
962
generated by this study and the insights gained via the most recent assessment of the regional
963
dietary patterns (Weber et al., 2011) as well as the more detailed study of the various
964
geochemical signatures assembled for the KN XIV cemetery in the Little Sea micro-region
965
(Weber et al., 2011). Given that the archaeological record is often incomplete or corrupted by
966
diagenetic and taphonomic factors, to find individuals from several sites, frequently separated by
967
a large distance that provide corroborating evidence is very encouraging as a form of
968
verification. This generally supports the utility of geochemical data for mobility studies and
969
provides additional justification to continue this line of investigation and to develop more
970
comprehensive models of hunter-gatherer mobility and travel.
971
The new geochemical data clearly support the notion about an asymmetry in the patterning of
972
hunter-gatherer movement between various micro-regions in Cis-Baikal (Weber and Goriunova,
973
2013; Weber et al., 2011). The mobility of individuals was heavily focused on movement and
974
interaction between groups residing in the Little Sea and on the Upper Lena. None of the 14
975
individuals analyzed in this study or 25 persons from KNXIV tested previously (Haverkort et al.,
976
2008), show any contact with the Angara drainage. While there was a large EBA population
977
living along the Angara, there is no evidence for significant movements of people from the
978
Angara to the Upper Lena or Little Sea.
979
The data from Khotoruk and Manzurka do not contradict the notion that asymmetrical
980
patterns of movement were also present during the EN but they do suggests that different
981
patterns of movement and cultural contact may have existed in the broader Cis-Baikal region
982
during the EN than during the LN–EBA. This inference, however, is based on a very small
983
sample of individuals and additional evidence is greatly needed to verify it. The only
984
geochemically demonstrable contact with an area close to Angara, which is with the valley of the
985
Kuda River, comes from one EN individual from Khotoruk but generally the samples analyzed
986
show significantly less overall mobility during the EN than during the EBA. This is intriguing
987
given the geography because the distances between the Little Sea and Upper Lena on one side
988
and the Angara valley on the other are quite substantial (ca. 250 km) but travel between the
989
Angara and the other two micro-regions along the Kuda and Manzurka rivers is relatively easy. 31
990
In contrast, the distance between the Little Sea and Upper Lena is much shorter (100–150 km)
991
but travel over the Primorskii Range Mountains could be quite difficult. While the geochemical
992
evidence for people’s movement between the Angara and the other two micro-regions is poor at
993
the moment, this does not mean that cultural contact did not exist at all, merely that the nature of
994
the contact probably was such as not to have been be recorded in geochemical tracers which is
995
quite informative and intriguing on its own.
996
The geochemical evidence for human travel between the Little Sea and Upper Lena micro-
997
regions is already ample and suggests that overall cultural nature of these connections was quite
998
extensive and complicated. Returning to the matter of EBA patterns, Shamanskii Mys data
999
contribute further to the interesting picture of mobility during this period. As already mentioned,
1000
based upon dietary evidence (Weber et al., 2011), there are two distinct diet types within the
1001
Little Sea EBA population: GF and GFS. Sealing is predominantly a seasonal activity conducted
1002
on Lake Baikal from late winter to early spring (Weber et al., 1998). Weber and colleagues
1003
(Weber and Goriunova, 2013; Weber et al., 2011), based on the examination of the large
1004
KNXIV data set, hypothesized the movement of significant numbers of individuals between the
1005
Upper Lena and Little Sea micro-regions on a seasonal basis and that the seasonal nature of this
1006
movement produced two cycles of inter-regional mobility, one that brought people from the
1007
Upper Lena to the Little Sea during the narrow window when seal meat was available for
1008
consumption and possibly to participate directly in the hunting of seals, and another pattern that
1009
brought non-locals to the Little Sea outside of the sealing season. Shamanskii Mys individuals
1010
(1973.003 and 1975.001) support the hypothesis that such inter-regional movements were not a
1011
unique feature of the KNXIV cemetery, but a broader phenomenon connecting the Upper Lena
1012
with the Little Sea as part of regular travel and interactions. What is interesting, however, and
1013
worth further research, is that at Shamanskii Mys there are no individuals with GF diet, while all
1014
mainland Little Sea cemeteries analyzed so far (KN XIV, Kurma XI and Sarminskii Mys, all
1015
relatively large burial grounds) have people with both diets (Weber et al., 2011).
1016
Even more intriguing is the correlation between inter-regional mobility and the presence of
1017
certain artifacts as grave goods. All three Shamanskii Mys individuals analyzed in this study
1018
were buried with nephrite artifacts, SHM_1973.003.01 and SHM_1975.001 each with white
1019
nephrite disks and SHM_1972.002 with a green nephrite knife (Konopatskii, 1982). This is
1020
interesting considering the likely provenance of these artifacts. Green nephrite comes from the 32
1021
Angara micro-region or the Eastern Sayan mountains while white nephrite comes from northern
1022
Trans-Baikal in the Vitim volcanic fields (Johnson et al., 2005; Sekerin and Sekerina, 2000). It
1023
has been suggested that there are far more sources of nephrite in the Baikal region (V.I.
1024
Bazaliiskii, personal communication), however, this research is still in progress and will
1025
hopefully identify the number of geochemically distinct nephrite sources. It is also intriguing that
1026
the local individual (SHM_1972.002) was buried with numerous grave goods including a green
1027
nephrite knife that was likely acquired indirectly through exchange or through a specific trek to
1028
procure raw material to make such an object, a trek that likely could only occur during
1029
adulthood. This individual had no connection with the Angara valley during their childhood,
1030
remaining firmly within the Little Sea, yet was buried with an exotic artifact from an area with
1031
which foragers in both the Upper Lena and the Little Sea had little direct contact. The other two
1032
individuals (SHM_1973.003.01 and SHM_1975.001) both of non-local birth and childhood and
1033
both were buried with white nephrite objects likely from Trans-Baikal.
1034
The highly seasonal seal hunt is an excellent candidate for contact across the lake with the
1035
people from Trans-Baikal including the Vitim volcanic fields with the white nephrite and/or
1036
other mineral resources. Modern Buryat residents of Ol’khon Island and the Little Sea are noted
1037
to have close kinship ties to groups around Barguzin in Trans-Baikal and retain contacts across
1038
the ice (R. Losey, personal communication). This cross-ice movement could explain the presence
1039
of white nephrite from northern Trans-Baikal in the Little Sea. Seal hunts could also function as
1040
trading excursions involving contacts with groups living on the eastern coast of the lake or
1041
further into Trans-Baikal. Having a highly prestigious item (nephrite) would go a long way
1042
towards explaining the nature of social contacts between the Upper Lena and the Little Sea. It is
1043
quite plausible that the long distance trade of valuables functioned as a strong or stronger draw-
1044
factor to make the trek across the taiga from the Upper Lena relative to the subsistence or
1045
prestige value of the seal hunt itself. Treks across the lake during the annual seal hunt provide an
1046
avenue for goods to flow, the sharing of seal meat along with other exchanges providing a
1047
measure of hospitality or pretext for the exchange of goods.
1048 1049
8. Conclusions
1050
Employment of geochemical tracers in studies of past hunter-gatherer mobility and migrations
1051
presents unique challenges but also offers equally unique opportunities. The advantage of Cis33
1052
Baikal for this kind of approaches is in that it has a long history of hunter-gatherers who
1053
maintained formal cemeteries which frequently yield well-preserved human skeletal remains, in
1054
an environment with significant geological and thus geochemical variation. Many other areas of
1055
the world do not have this advantageous combination of human skeletal materials and
1056
environment in which to address questions of mobility with geochemical methods. Foragers are
1057
frequently highly mobile and can have seasonally diverse diets. This makes methods developed
1058
for sedentary agrarian populations not applicable directly, hence the need for new approaches.
1059
Previous applications of strontium isotopes to track mobility in Cis-Baikal hunter-gatherer
1060
groups showed promise and the adoption of the local versus non-local distinction for the Little
1061
Sea micro-region quite suitable to monitor the movements of people throughout the region. Upon
1062
further investigation however, the highly variable distribution of biologically available strontium
1063
ratios (87Sr/86Sr) reveal a very complex geochemical environment that prehistoric Baikal foragers
1064
were in contact with. Consequently, to track human mobility more effectively the original
1065
approach required augmentation using trace element analysis. This tandem geochemical
1066
approach proved highly effective tool for discriminating micro-regions with overlapping
1067
87
Sr/86Sr ratios as well as for provenancing prehistoric people.
1068
Our ability to track hunter-gatherer mobility is entirely dependent on the resolution of
1069
geochemical surveys. The greater the resolution of regional environmental sampling, the better
1070
the chance that analysis of individual provenance data can reach beyond broad geologic zones
1071
and enters discussions about inter-micro-regional movements. Individual mobility patterns
1072
appear to be quite variable thus supporting the notion that Neolithic and EBA Baikal hunter-
1073
gatherers were quite mobile and not tied firmly to the very productive fisheries in the Little Sea
1074
area. Already a large number of individuals show evidence of significant movement and
1075
interactions occurring between the Upper Lena and the Little Sea micro-regions. Interestingly,
1076
there seems to be very little evidence in support of the frequently implicitly assumed systematic
1077
intermixing of groups from the Angara River on one side and the Upper Lena and Little Sea on
1078
the other.
1079
The apparent lack of the gradual transition in carbon and nitrogen stable isotope signatures
1080
from the GFS to GF diet is perplexing because one would expect exactly such gradual shift if
1081
people were moving from one area to another and, with time, incorporating into their bone tissue
1082
geochemical signatures characteristic of the foods available close to new “home”. The notion 34
1083
that all Little Sea individuals with GF diet simply have diets originating in the Upper Lena
1084
without any contribution of the aquatic foods available in the Little Sea, of course, requires more
1085
work to demonstrate to be true but already raises a host of most puzzling questions about the
1086
nature of this cultural contact: Why did some people move to the Little Sea and stay there and
1087
some kept returning to the Upper Lena? Why some appear to move there to die or did they die
1088
elsewhere and where moved to the Little Sea for burial only and, if so, why? Did they die a
1089
violent death at the hands of the hostile locals? If so, why were they buried together with the rest:
1090
locals and non-locals? It is not necessary to attempt answers to such and other similar questions
1091
in this paper but it is clear that all this work is crossing new boundaries of archaeological inquiry.
1092
Overall then, given how incomplete the archaeological record is, it is intriguing however that
1093
we have managed to identify several individuals involved in now confirmed inter-regional
1094
movements between the Upper Lena and the Little Sea. As such, the combined use of strontium
1095
and trace-element geochemical markers has the potential to provide many new insights on the
1096
broad regional scale facilitated by the large Cis-Baikal cemeteries such as Lokomotiv, Ust’-Ida,
1097
Shamanka II, KNXIV and Kurma XI.
1098 1099
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Shouakar-Stash, O., Alexeev, S.V., Frape, S.K., Alexeeva, L.P. and Drimmie, R.J., 2007.
1319
Geochemistry and stable isotopic signatures, including chlorine and bromine isotopes, of
1320
the deep groundwaters of the Siberian Platform, Russia Applied Geochemistry 22 (3),
1321
589-605.
1322
Sillen, A., Hall, G., Richardson, S. and Armstrong, R., 1998. 87Sr/86Sr ratios in modern and
1323
fossil food-webs of the Sterkfontein Valley: implications for early hominid habitat
1324
preference Geochimica et Cosmochimica Acta 62 (14), 2463-2473.
1325
Simonetti, A., Buzon, M.R. and Creaser, R.A., 2008. In-situ elemental and Sr isotope
1326
investigation of human tooth enamel by laser-ablation-(MC)-ICP-MS: successes and
1327
pitfalls Archaeometry 50 (2), 371-385.
1328 1329
Suga, S., 1982. Progressive mineralization pattern of developing enamel during the maturation stage Journal of Dental Research 61, 1532-1542.
1330
Suga, S., 1989. Enamel hypomineralization viewed from the pattern of progressive
1331
mineralization of human and moneky developing enamel Advances in Dental Research 3,
1332
188-198.
1333
Tafforeau, P., Bentaleb, I., Jaeger, J.-J. and Martin, C., 2007. Nature of laminations and
1334
mineralization in rhinoceros enamel using histology and X-ray synchrotron
1335
microtomography: Potential implications for palaeoenvironmental isotopic studies
1336
Palaeogeography, Palaeoclimatology, Palaeoecology 246 (2-4), 206-227.
1337 1338
Trotter, J.A. and Eggins, S.M., 2006. Chemical systematics of conodont apatite determined by laser ablation ICP-MS Chemical Geology 233, 196-216.
1339
Truncer, J., Glascock, M.D. and Neff, H., 1998. Steatite Source Characterization in Eastern
1340
North America: New Results Using Instrumental Neutron Activation Analysis
1341
Archaeometry 40, 23-44. 44
1342
Vroon, P.Z., van der Wagt, B. and Koornneef, J.M., 2008. Problems in obtaining precise and
1343
accurate Sr isotope analysis from geological materials using laser ablation MC-ICPMS
1344
Analytical and Bioanalytical Chemistry 390 (465-476).
1345
Weber, A.W., 2003. Biogeographic profile of the Lake Baikal region, Siberia. In: A. W. Weber
1346
and H. McKenzie (Eds.), Prehistoric Foragers of the Cis-Baikal, Siberia, Proceedings of
1347
the First Conference of the Baikal Archaeology Project, pp. 51-66. Canadian Circumpolar
1348
Institute Press, Edmonton.
1349
Weber, A.W., Beukens, R., Bazaliiski, V.I., Goriunova, O.I. and Savel’ev, N.A., 2006.
1350
Radiocarbon dates from Neolithic and Bronze Age hunter-gatherer cemeteries in the Cis-
1351
Baikal region of Siberia Radiocarbon 48 (1), 1-40.
1352
Weber, A.W., Creaser, R.A., Goriunova, O.I. and Haverkort, C.M., 2003. Strontium isotope
1353
tracers in enamel of permanent Human molars provide new insights into prehistoric
1354
hunter-gatherer procurement and mobility patterns: a pilot study of a Middle Holocene
1355
group from Cis-Baikal. In: A. W. Weber and H. McKenzie (Eds.), Prehistoric Foragers of
1356
the Cis-Baikal, Siberia, Proceedings of the First Conference of the Baikal Archaeology
1357
Project, pp. 133-153. Canadian Circumpolar Institute Press, Edmonton.
1358
Weber, A.W. and Goriunova, O.I., 2013. Hunter–gatherer migrations, mobility and social
1359
relations: A case study from the Early Bronze Age Baikal region, Siberia Journal of
1360
Anthropological Archaeology 32 (3), 330-346.
1361
Weber, A.W., Link, D.W., Goriunova, O.I. and Konopatskii, A.K., 1998. Patterns of Prehistoric
1362
Procurement of Seal at Lake Baikal: A Zooarchaeological Contribution to the Study of
1363
Past Foraging Economies in Siberia Journal of Archaeological Science 25 (3), 215-227.
1364
Weber, A.W., Link, D.W. and Katzenberg, M.A., 2002. Hunter-Gatherer Culture Change and
1365
Continuity in the Middle Holocene of the Cis-Baikal, Siberia Journal of Anthropological
1366
Archaeology 21 (2), 230-299.
1367
Weber, A.W., White, D., Bazaliiski, V.I., Goriunova, O.I., Savel’ev, N.A. and Katzenberg, M.A.,
1368
2011. Hunter-gatherer foraging ranges, migrations, and travel in the middle Holocene 45
1369
Baikal region of Siberia: Insights from carbon and nitrogen stable isotope signatures
1370
Journal of Anthropological Archaeology In Press.
1371
Weber, A.W., White, D., Bazaliiskii, V.I., Goriunova, O.I., Savel'ev, N.A. and Anne Katzenberg,
1372
M., 2011. Hunter-gatherer foraging ranges, migrations, and travel in the middle Holocene
1373
Baikal region of Siberia: Insights from carbon and nitrogen stable isotope signatures
1374
Journal of Anthropological Archaeology 30 (4), 523-548.
1375
Woodhead, J.D., Swearer, S., Hergt, J. and Maas, R., 2005. In situ Sr-isotope analysis of
1376
carbonates by LA-MC-ICP-MS: interference corrections, high spatial resolution and an
1377
example from otolith studies Journal of Analytical Atomic Spectrometry 20, 22-27.
1378 1379
Figure captions:
1380
Fig. 1. Map of the Cis-Baikal, Siberia region, main geological formations, and archaeological
1381
micro-regions.
1382
Fig. 2. Map of the Cis-Baikal region, locations of middle Holocene cemeteries used in this study
1383
(named), and sites of environmental (water and plant) sampling.
1384
Fig. 3. Diagram of a molar showing location and spacing of ablation lines on a tooth from crown
1385
to cingulum.
1386
Fig. 4. Stable isotope results for the individuals examined in the study (based on data in Table 3).
1387
Fig. 5. Stable isotope results for middle Holocene foragers from the Little Sea and Upper Lena
1388
micro-regions (after Weber et al., 2011). Note: Results for the Turuka early Neolithic cemetery,
1389
located on far north end of the Upper Lena area are not included.
1390
Fig. 6. Stable isotope results for middle Holocene foragers from the Little Sea and Upper Lena
1391
micro-regions (based on data in Table 3) including the results displayed in Fig. 3.
1392
Fig. 7. Stable isotope results for 24 middle Holocene foragers from the Khuzhir-Nuge XIV
1393
cemetery, Little Sea, examined for strontium isotope signatures (based on data in Table 2, Weber
1394
and Goriunova 2012). 46
87
Sr/86Sr isotopic distributions throughout the Cis-Baikal
1395
Fig. 8. Approximate gradient map of
1396
region and locations of environmental (water and plant) sampling sites.
1397
Fig. 9. Bivariate plot showing the results of Principal Components Analysis employing complete
1398
isotopic and trace element data for environmental samples grouped by micro-region.
1399
Fig. 10. Bivariate plot showing the results of Discriminant Function Analysis employing
1400
complete isotopic and trace element data for environmental samples grouped by micro-region.
1401
Fig. 11. Bivariate plot showing the results of Discriminant Function Analysis employing
1402
complete isotopic and trace element data for environmental samples grouped by micro-regional
1403
subranges as determined by cluster analysis.
1404
Fig. 12. Bivariate plot showing the results of Discriminant Function Analysis employing trace
1405
element data for environmental samples grouped by micro-region within the
1406
0.708–0.710.
1407
Fig. 13. Bivariate plot showing the results Discriminant Function Analysis employing trace
1408
element data for environmental samples grouped by micro-regional subclass projected along
1409
with the results for MNZ_1974.002.
1410
Fig.
1411
SHM_1973.003.01 individuals projected against environmental data, demonstrating their
1412
provenance based on geochemical tracers.
14.
Bivariate
plots
for
BO1_1971.001,
OBK_1971.005,
87
Sr/86Sr range of
OBK_1971.007,
and
47
a b c d
25
20
δ15N
15
10
5
0 -35
-30
-25
δ13C
-20
-15
-10
25
20
δ15N
15
10
5
0 -35
-30
-25
δ13C
-20
-15
-10
25
20
δ15N
15
10
5
0 -35
-30
-25
δ13C
-20
-15
-10
25
20
δ15N
15
10
5
0 -35
-30
-25
δ13C
-20
-15
-10
Principal Component 3
Tunka
Little Sea
Angara Upper Lena
Principal Component 1
Little Sea
Canonical Discriminant 3
Tunka
Angara Upper Lena
Canonical Discriminant 1
Upper Lena 4 Angara 2 Little Sea 1
Canonical Discriminant 3
Upper Lena 2 Upper Lena 1
Upper Lena 3 Tunka 3
Angara 1 Tunka 1
Tunka 2
Little Sea 2
Canonical Discriminant 1
Canonical Discriminant 2
Angara Little Sea Tunka Upper Lena
Canonical Discriminant 1
Canonical Discriminant 3
Angara Little Sea Tunka Upper Lena MNZ_1974.002
Canonical Discriminant 1
Canonical Discriminant 2 Canonical Discriminant 2
3
2
3
Borki 1
Little Sea (1) Tunka (2) Angara (3) Upper Lena (4)
2
2
Human Samples
0
0
4
1
3
2
Human Samples
1
1
Obkhoi 5
Little Sea (1) Tunka (2) Angara (3) Upper Lena (4)
-1
-1
-2 -6
-2
4 3 1
-5
-4
-3
-2
-1
0
3
2
2
-4
-3
-2
-1
3
0
4
-1
-1 1 -2
-1
2
1 1
-2
1
2 3
0
0
Shamanskii Mys 3
Little Sea (1) Tunka (2) Angara (3) Upper Lena (4)
2
Human Samples
1
-5 3
Obkhoi 7
Little Sea (1) Tunka (2) Angara (3) Upper Lena (4)
2
1
0
Human Samples
4 1
2
3
Canonical Discriminant 1
4
5
-2
-2
-1
0
1
2
3
Canonical Discriminant 1
4
5
Table 1 Archaeological information about individuals examined in this study. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14
SITE Manzurka Borki 1 Obkhoi Obkhoi Obkhoi Khotoruk Khotoruk Khotoruk Khotoruk Khotoruk Khotoruk Shamanskii Mys Shamanskii Mys Shamanskii Mys
Microregion Lena Lena Lena Lena Lena Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea
MASTER_ID MNZ_1974.002 BO1_1971.001 OBK_1971.005 OBK_1971.007 OBK_1971.013 KHO_1977.002 KHO_1978.004.01 KHO_1978.004.02 KHO_1978.004.03 KHO_1978.004.04 KHO_1978.007 SHM_1972.002 SHM_1973.003.01 SHM_1975.001
Sex Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Male Probable female Male
Age 14-18/19 years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 36-50/55 years 20+ years 20-30/35 years
Teeth analyzed M1, M2, M3 M1, M2, M3 M1, M2, M3 M1, M2 M1, M2, M3 M1, M2, M3 M1, M2, M3 M1, M2, M3 M1, M2 M1, M2, M3 M1, M2, M3 M1, M2, M3 M2, M3 M1, M2, M3
Archaeological age Early Neolithic Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Neolithic Early Neolithic Early Neolithic Early Neolithic Early Neolithic Early Neolithic Early Bronze Age Early Bronze Age Early Bronze Age
Table 2 Environmental samples (i.e., water, plants, animals) used in the study. Sample ID
Site
Latin Name
English Name
Russian Name
Element
Latitude, °N
Longitude, °E
2008.008 2008.013 2008.014 2008.015 2008.016 2008.017 2008.018 2008.019 2008.020 2008.021 2008.030 2008.035 2008.039 2008.040 2008.051 2008.056 2008.057 2008.061 2008.065 2008.068 2008.069 2008.070 2008.071 2008.072 2008.074 2008.075 2008.076 2008.080 2008.082 2008.085 2008.086 2008.088 2008.089 2008.091 2008.092 2008.094 2008.095 2008.105 2008.112 2008.115 2008.117 2008.118 2008.122 2008.126 2008.128 2008.129 2008.130 2008.131 2009.001 2009.002 2009.007 2009.008 2009.018 2009.019 2009.023 2009.029 2009.030 2009.034 2009.040 2009.041 2009.050 2009.051 2009.055 2009.056 2009.057 2009.061 2009.062 2009.073 2009.074 2009.078 2009.079 2009.082 2009.083 2009.086 2009.088 2009.089 2009.094 2009.095
Arshan Arshan Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Tunka Valley Little Sea Little Sea Little Sea Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Ol'khon Island Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Sagan-Zaba Baiandai Baiandai Khogot Khogot Khogot Khogot Khodontsa R. Manzurka Manzurka Manzurka R. Kharbatovo Kharbatovo Ilikta Ilikta Ilikta R. Lena R. Biriulka R. Makrushina Makrushina Malye Goly Malye Goly Anga R. Tutura R. Zhigalovo Zhigalovo Lena R. Vorob'eva Vorob'eva Zapleskino Zapleskino
Martes zibellina Apodemus peninsulae Citelius parryi Bos taurus Bos taurus Bos taurus Bos taurus Bos taurus Bos taurus Bos taurus Equus callabus Equus callabus Equus callabus Equus callabus Cervidae Citelius parryi Citelius parryi Lepus Citelius parryi Citelius parryi Citelius parryi Citelius parryi Citelius parryi Citelius parryi Citelius parryi Citelius parryi Citelius parryi Citelius parryi Capreolus pygargus Capreolus pygargus Capreolus pygargus Capreolus pygargus Cervus elaphus Cervidae Capreolus pygargus Capreolus pygargus Phoca sibirica Cervus elaphus Citelius parryi Capreolus pygargus Capreolus pygargus Citelius parryi Capreolus pygargus Capreolus pygargus Citelius parryi Apodemus peninsulae Citelius parryi Citelius parryi Betula sp. Betula sp. Picea sp. Picea sp. Betula sp. Abies sp. n/a Betula sp. Abies sp. n/a Betula sp. Pinus sp. Betula sp. Picea sp. n/a n/a n/a Betula sp. Pinus sp. Betula sp. Pinus sp. n/a n/a Betula sp. Pinus sp. n/a Betula sp. Pinus sp. Betula sp. Pinus sp.
sable mouse ground squirrel cow cow cow cow cow cow cow horse horse horse horse deer ground squirrel ground squirrel hare ground squirrel ground squirrel ground squirrel ground squirrel ground squirrel ground squirrel ground squirrel ground squirrel ground squirrel ground squirrel roe deer roe deer roe deer roe deer red deer deer roe deer roe deer baikal seal red deer ground squirrel roe deer roe deer ground squirrel roe deer roe deer ground squirrel mouse ground squirrel ground squirrel birch birch spruce spruce birch larch n/a birch larch n/a birch pine birch spruce n/a n/a n/a birch pine birch pine n/a n/a birch pine n/a birch pine birch pine
sobol' mysh suslik korova korova korova korova korova korova korova loshad' loshad' loshad' loshad' olen' suslik suslik zajac suslik suslik suslik suslik suslik suslik suslik suslik suslik suslik kosulia kosulia kosulia kosulia blagorodnyi olen' olen' kosulia kosulia baikal'skaia nerpa blagorodnyi olen' suslik kosulia kosulia suslik kosulia kosulia suslik mysh suslik suslik bereza bereza el' el' bereza listvennitsa n/a bereza listvennitsa n/a bereza sosna bereza el' n/a n/a n/a bereza sosna bereza sosna n/a n/a bereza sosna n/a bereza sosna bereza sosna
tooth tooth longbone tooth longbone tooth tooth tooth tooth tooth tooth longbone longbone tooth pedal element tooth vertebra longbone tooth tooth longbone tooth tooth tooth inominate longbone tooth tooth tooth pedal element tooth tooth tooth longbone tooth tooth rib longbone longbone tooth tooth tooth tooth tooth tooth tooth tooth tooth leaves leaves needles needles leaves needles water leaves needles water leaves needles leaves needles water water water leaves needles leaves needles water water leaves needles water leaves needles leaves needles
51°53'52" 51°54'54" 51°42'23" 51°45'32" 51°54'91" 51°41'56" 51°41'57" 51°41'57" 51°41'57" 51°41'57" 51°41'57" 51°41'57" 51°41'57" 51°41'57" 53°05'54" 53°04'98" 53°04'29" 53°00'87" 53°00'92" 53°01'74" 53°01'75" 53°01'75" 53°01'76" 53°01'77" 53°01'78" 53°01'80" 53°01'80" 53°01'56" 52°41'60" 52°41'32" 52°41'29" 52°41'39" 52°41'40" 52°41'46" 52°41'47" 52°41'48" 52°38'82" 52°40'89" 52°43'56" 52°43'56" 52°43'54" 52°43'49" 52°43'48" 52°42'51" 52°41'33" 52°41'53" 52°41'37" 52°41'32" 53°07'13" 53°07'13" 53°13'40" 53°13'40" 53°16'56" 53°16'56" 53°22'33" 53°30'26" 53°30'26" 53°43'24" 53°48'31" 53°48'31" 53°49'25" 53°49'25" 53°49'25" 53°50'22" 53°51'54" 53°52'50" 53°52'50" 53°54'38" 53°54'38" 53°56'14" 54°47'23" 54°46'07" 54°46'07" 54°39'54" 54°34'38" 54°34'38" 54°21'58" 54°21'58"
102°26'08" 102°23'23" 102°35'50" 102°33'41" 102°23'37" 102°33'49" 102°25'34" 102°25'34" 102°25'34" 102°25'34" 102°25'34" 102°25'34" 102°25'34" 102°25'34" 106°48'09" 106°46'62" 106°46'62" 106°56'20" 106°56'14" 106°56'28" 106°56'30" 106°56'30" 106°56'30" 106°56'32" 106°56'35" 106°56'36" 106°56'36" 106°56'40" 106°27'76" 106°29'21" 106°29'18" 106°29'26" 106°29'15" 106°29'10" 106°29'10" 106°29'06" 106°26'06" 106°25'48" 106°31'62" 106°31'24" 106°31'18" 106°31'05" 106°30'96" 106°29'33" 106°29'07" 106°28'00" 106°28'26" 106°26'36" 105°38'31" 105°38'31" 105°51'04" 105°51'04" 105°54'18" 105°54'18" 106°00'24" 106°01'37" 106°01'37" 105°59'37" 105°58'47" 105°58'47" 106°24'54" 106°24'54" 106°24'54" 106°20'41" 106°20'18" 106°15'40" 106°15'40" 106°09'29" 106°09'29" 106°02'49" 105°14'06" 105°08'39" 105°08'39" 105°13'05" 105°14'48" 105°14'48" 105°15'00" 105°15'00"
2009.099 2009.101 2009.102 2009.108 2009.109 2009.112 2009.114 2009.115 2009.119 2009.120 2009.123 2009.124 2009.128 2009.129 2009.134 2009.135 2009.139 2009.140 2009.141 2009.142 2009.145 2009.146 2009.150 2009.151 2009.155 2009.157 2009.161 2009.163 2009.164 2009.168 2009.169 2009.170 2009.172 2009.173 2009.175 2009.176 2009.177 2009.182 2009.185 2009.186 2009.191 2009.192 2009.197 2009.198 2009.203 2009.204 2009.207 2009.208 2009.211 2009.212 2009.215 2009.216 2009.219 2009.220 2009.225 2009.226 2009.229 2009.232 2009.233 2009.236 2009.239 2009.240 2009.245 2009.246 2009.248 2009.251 2009.252 2009.257 2009.258 2009.261 2009.262 2009.263 2009.265 2009.266 2009.267 2009.269 2009.270 2009.271 2009.273 2009.274 2009.275 2009.277
Gul'ma R. Verkholensk Verkholensk Magdan Magdan Magdan R. Magdan Magdan Kulenga R. Inei R. Ikhinagui Ikhinagui Obkhoi Obkhoi Shemetovo Shemetovo Shemetovo Shemetovo Tal'ma R. Kulenga R. Kartukhai Kartukhai Shyshkino Shyshkino Mys Mys Anga R. Kachug Kachug Lena R. Polovinka Polovinka Oloi Oloi Kuda R. Sosnovyi Bor Sosnovyi Bor Irkut R. Moty Moty Andrianovskaia Andrianovskaia Angaselka Angaselka Kultuk Kultuk Kultuchnaia R Bol'shaia Bystraia R. Bystroe Bystroe Sredniaia Tibelti R. Irkut R. Tibelti Tibelti Zun-Murino Zun-Murino Zun-Murino R. Shabartaika Shabartaika Irkut R. Guzhiry Guzhiry Dalakhai Dalakhai Tsagan-Ugun R. Nikol'sk Nikol'sk Tagarkhai Tagarkhai Kitoi R. Il'chir Lake Il'chir Lake Ch. Irkut R. Il'chir Lake Il'chir Lake Suser R. Ch. Irkut Ch. Irkut Ch. Irkut R. Ch. Irkut Ch. Irkut B. Irkut
n/a Betula sp. Pinus sp. Betula sp. Abies sp. n/a Betula sp. Pinus sp. n/a n/a Betula sp. Abies sp. Betula sp. Pinus sp. Betula sp. Pinus sp. Bos taurus Bos taurus n/a n/a Betula sp. Pinus sp. Betula sp. Pinus sp. Betula sp. Picea sp. n/a Betula sp. Abies sp. n/a Betula sp. Betula sp. Betula sp. Betula sp. n/a Betula sp. Betula sp. n/a Betula sp. Pinus sp. Betula sp. Pinus sp. Betula sp. Pinus sp. Betula sp. Betula sp. n/a n/a Betula sp. Pinus sp. n/a n/a Betula sp. Pinus sp. Betula sp. Pinus sp. n/a Betula sp. Pinus sp. n/a Betula sp. Pinus sp. Betula sp. Betula sp. n/a Picea sp. Pinus sp. Betula sp. Abies sp. n/a Abies sp. Abies sp. n/a Abies sp. Abies sp. n/a Abies sp. Abies sp. n/a Betula sp. Betula sp. Betula sp.
n/a birch pine birch larch n/a birch pine n/a n/a birch larch birch pine birch pine cow cow n/a n/a birch pine birch pine birch spruce n/a birch larch n/a birch birch birch birch n/a birch birch n/a birch pine birch pine birch pine birch birch n/a n/a birch pine n/a n/a birch pine birch pine n/a birch pine n/a birch pine birch birch n/a spruce pine birch larch n/a larch larch n/a larch larch n/a larch larch n/a birch birch birch
n/a bereza sosna bereza listvennitsa n/a bereza sosna n/a n/a bereza listvennitsa bereza sosna bereza sosna korova korova n/a n/a bereza sosna bereza sosna bereza el' n/a bereza listvennitsa n/a bereza bereza bereza bereza n/a bereza bereza n/a bereza sosna bereza sosna (kedr) bereza sosna bereza bereza n/a n/a bereza sosna (kedr) n/a n/a bereza sosna bereza sosna n/a bereza sosna n/a bereza sosna bereza bereza n/a el' sosna bereza listvennitsa n/a listvennitsa listvennitsa n/a listvennitsa listvennitsa n/a listvennitsa listvennitsa n/a bereza bereza bereza
water leaves needles leaves needles water leaves needles water water leaves needles leaves needles leaves needles molar molar water water leaves needles leaves needles leaves needles water leaves needles water leaves leaves leaves leaves water leaves leaves water leaves needles leaves needles leaves needles leaves leaves water water leaves needles water water leaves needles leaves needles water leaves needles water leaves needles leaves leaves water needles needles leaves needles water needles needles water needles needles water needles needles water leaves leaves leaves
54°12'53" 54°06'44" 54°06'44" 53°37'34" 53°37'34" 53°37'34" 53°37'41" 53°37'41" 53°40'41" 53°43'12" 53°44'32" 53°44'32" 53°58'34" 53°58'34" 54°01'03" 54°01'03" 54°01'03" 54°01'03" 54°01'12" 54°06'01" 54°02'17" 54°02'17" 54°00'07" 54°00'07" 54°01'55" 54°01'55" 53°58'41" 53°58'53" 53°58'53" 53°56'38" 53°07'07" 53°07'07" 52°55'09" 52°55'09" 52°55'56" 52°39'01" 52°39'01" 52°04'34" 52°04'24" 52°04'24" 51°48'34" 51°48'34" 51°43'21" 51°43'21" 51°44'04" 51°44'04" 51°44'04" 51°43'51" 51°43'56" 51°43'56" 51°45'41" 51°46'39" 51°46'45" 51°46'45" 51°44'07" 51°44'07" 51°43'56" 51°43'01" 51°43'01" 51°43'03" 51°47'42" 51°47'42" 51°48'01" 51°48'01" 51°48'01" 51°43'06" 51°43'06" 51°52'32" 51°52'32" 52°02'56" 52° 0' 2" 52° 0' 2" 51°57'23" 51°57'39" 51°57'39" 51°54'20" 51°54'04" 51°54'04" 51°54'04" 51°47'54" 51°47'54" 51°46'12"
105°27'08" 105°34'06" 105°34'06" 105°17'56" 105°17'56" 105°17'56" 105°17'21" 105°17'21" 105°19'16" 105°20'03" 105°19'53" 105°19'53" 105°26'47" 105°26'47" 105°26'47" 105°26'47" 105°26'47" 105°26'47" 105°29'57" 105°35'05" 105°37'17" 105°37'17" 105°43'48" 105°43'48" 106°24'15" 106°24'15" 106°11'15" 105°51'41" 105°51'41" 105°53'04" 105°40'44" 105°40'44" 105°10'37" 105°10'37" 104°51'25" 104°30'59" 104°30'59" 103°53'34" 103°54'14" 103°54'14" 103°47'18" 103°47'18" 103°42'02" 103°42'02" 103°38'12" 103°38'12" 103°38'12" 103°23'13" 103°25'57" 103°25'57" 103°15'17" 103°14'41" 103°13'19" 103°13'19" 102°52'41" 102°52'41" 102°51'40" 102°43'43" 102°43'43" 102°35'13" 102° 4' 3" 102° 4' 3" 102°58'31" 102°58'31" 102°58'31" 102°35'23" 102°35'23" 102°26'41" 102°26'41" 101°05'53" 101°01'17" 101°01'17" 100°56'35" 100°57'05" 100°57'05" 100°45'27" 100°45'30" 100°45'30" 100°45'30" 100°42'32" 100°42'32" 100°42'33"
2009.278 2009.280 2009.281 2009.282 2009.284 2009.286 2009.287 2009.289 2009.290 2009.293 2009.294 2009.297 2009.300 2009.301 2009.304 2009.307 2009.308 2009.311 2009.314 2009.315 2009.318 2009.321 2009.322 2009.325 2009.326 2009.327 2009.329 2009.330 2009.331 2009.335 2009.336 2009.339 2009.342 2009.343 2009.346 2009.347 2009.349 2009.352 2009.353 2009.356 2009.357 2009.360 2009.361 2009.364 2009.365 2009.367 2009.368 2009.370 2009.373 2009.374 2009.379 2009.380 2009.385 2009.386 2009.391 2009.392 2009.395 2009.396 2009.399 2009.400 2009.403 2009.406 2009.407 2009.412 2009.413 2009.416 2009.419 2009.420 2009.423 2009.426 2009.427 2009.432 2009.433 2009.436 2009.437 2009.438 2009.440 2009.441 2009.443 2009.444 2009.445 2009.447
B. Irkut B. Irkut R. Irkut Irkut Irkut R. Aerkhan Aerkhan Aerkhan R. Irkut R. B. Khara-Gol B. Khara-Gol B. Khara-Gol R. Khalagan Khalagan Khalagan R. Nilovka Nilovka Ekhe-Ukhgun' R. Turan Turan Irkut R. B. Zangisan B. Zangisan B. Zangisan R. Arshan Arshan Kyngarga R. Arshan Arshan Arshan Arshan Tunka R. Tunka Tunka Ulan-Gorkhon Ulan-Gorkhon Ulan-Gorkhon R. Okhor-Shibir Okhor-Shibir Okhor-Shibir R. Irkut R. Kiren Kiren Kultuk Kultuk Bezymiannaia Bezymiannaia Bezymiannaia R. Sukhoi Ruchei Sukhoi Ruchei Gramatukha Gramatukha Bulga Bulga Kosaia Step Kosaia Step Bugul'deika R. Bugul'deika R. Dolon-Bogot Dolon-Bogot Dolon-Bogot R. Popovo Popovo Petrovo Petrovo Anga R. Khotoruk Khotoruk Gorkhon R. Narim-Kurei Narim-Kurei Shamanskii Mys Shamanskii Mys Baikal Lake Khuzhir Khuzhir Khadai Khadai Baikal Lake Kurkut Bay Kurkut Bay Kurma
Betula sp. n/a Abies sp. Abies sp. n/a Abies sp. Pinus sp. n/a n/a Betula sp. Pinus sp. n/a Betula sp. Abies sp. n/a Betula sp. Pinus sp. n/a Betula sp. Pinus sp. n/a Betula sp. Pinus sp. n/a Pinus sp. Pinus sp. n/a Abies sp. Abies sp. Betula sp. Pinus sp. n/a Betula sp. Pinus sp. Betula sp. Betula sp. n/a Betula sp. Larix sp. n/a n/a Betula sp. Pinus sp. Betula sp. Betula sp. Betula sp. Betula sp. n/a Betula sp. Pinus sp. Pinus sp. Abies sp. Betula sp. Larix sp. Betula sp. Larix sp. n/a n/a Betula sp. Pinus sp. n/a Betula sp. Larix sp. Betula sp. Pinus sp. n/a Larix sp. Pinus sp. n/a Larix sp. Pinus sp. Larix sp. Larix sp. n/a Larix sp. Larix sp. Larix sp. Larix sp. n/a Larix sp. Larix sp. Larix sp.
birch n/a larch larch n/a larch pine n/a n/a birch pine n/a birch larch n/a birch pine n/a birch pine n/a birch pine n/a pine pine n/a larch larch birch pine n/a birch pine birch birch n/a birch larch n/a n/a birch pine birch birch birch birch n/a birch pine pine fir birch larch birch larch n/a n/a birch pine n/a birch larch birch pine n/a larch pine n/a larch pine larch larch n/a larch larch larch larch n/a larch larch larch
bereza n/a listvennitsa listvennitsa n/a listvennitsa sosna n/a n/a bereza sosna (kedr) n/a bereza listvennitsa n/a bereza sosna n/a bereza sosna n/a bereza sosna n/a sosna sosna n/a listvennitsa listvennitsa bereza sosna n/a bereza sosna bereza bereza n/a bereza listvennitsa n/a n/a bereza sosna bereza bereza bereza bereza n/a bereza sosna sosna pikhta bereza listvennitsa bereza listvennitsa n/a n/a bereza sosna n/a bereza listvennitsa bereza sosna n/a listvennitsa sosna n/a listvennitsa sosna listvennitsa listvennitsa n/a listvennitsa listvennitsa listvennitsa listvennitsa n/a listvennitsa listvennitsa listvennitsa
leaves water needles needles water needles needles water water leaves needles water leaves needles water leaves needles water leaves needles water leaves needles water needles needles water needles needles leaves needles water leaves needles leaves leaves water leaves needles water water leaves needles leaves leaves leaves leaves water leaves needles needles needles leaves needles leaves needles water water leaves needles water leaves needles leaves needles water needles needles water needles needles needles needles water needles needles needles needles water needles needles needles
51°46'12" 51°46'12" 51°44'58" 51°44'58" 51°44'58" 51°41'50" 51°41'50" 51°41'50" 51°40'26" 51°39'16" 51°39'16" 51°39'16" 51°37'56" 51°37'56" 51°37'56" 51°40'45" 51°40'45" 51°40'45" 51°38'55" 51°38'55" 51°38'55" 51°40'00" 51°40'00" 51°40'00" 51°55'02" 51°55'02" 51°55'02" 51°55'06" 51°55'06" 51°52'06" 51°52'06" 51°44'24" 51°44'20" 51°44'20" 51°41'23" 51°41'23" 51°41'23" 51°37'55" 51°37'55" 51°37'55" 51°42'49" 51°40'34" 51°40'34" 51°41'15" 51°41'15" 51°35'42" 51°35'42" 51°35'42" 51°37'37" 51°37'37" 51°56'46" 51°56'46" 52°58'04" 52°58'04" 52°52'03" 52°52'03" 52°50'27" 52°32'52" 52°36'23" 52°36'23" 52°36'23" 52°43'49" 52°43'49" 52°44'59" 52°44'59" 52°47'57" 52°47'50" 52°47'50" 52°51'01" 52°50'43" 52°50'43" 53°12'11" 53°12'11" 53°12'11" 53°10'40" 53°10'40" 53°03'52" 53°03'52" 53°01'05" 53°00'39" 53°00'39" 53°10'59"
100°42'33" 100°42'33" 100°43'56" 100°43'56" 100°43'56" 100°52'18" 100°52'18" 100°52'18" 101°01'06" 101°15'05" 101°15'05" 101°15'05" 101°33'23" 101°33'23" 101°33'23" 101°40'46" 101°40'46" 101°40'46" 101°41'12" 101°41'12" 101°41'12" 101°49'43" 101°49'43" 101°49'43" 102°25'34" 102°25'34" 102°25'34" 102°25'25" 102°25'25" 102°30'14" 102°30'14" 102°31'53" 102°30'11" 102°30'11" 102°30'41" 102°30'41" 102°30'41" 102°21'50" 102°21'50" 102°21'50" 102°25'50" 102°08'49" 102°08'49" 103°41'37" 103°41'37" 103°54'34" 103°54'34" 103°54'34" 103°47'45" 103°47'45" 103°48'42" 103°48'42" 105°44'18" 105°44'18" 106°01'55" 106°01'55" 106°04'58" 106°03'49" 106°08'42" 106°08'42" 106°08'42" 106°17'28" 106°17'28" 106°21'01" 106°21'01" 106°30'59" 106°30'50" 106°30'50" 106°28'51" 106°29'16" 106°29'16" 107°20'36" 107°20'36" 107°20'36" 107°20'03" 107°20'03" 107°03'54" 107°03'54" 106°53'47" 106°48'15" 106°48'15" 106°58'27"
2009.448 2009.450 2009.451 2009.452 2009.454 2009.455 2009.456 2009.460 2009.461 2009.464 2009.467 2009.468 2009.473 2009.474 2009.477 2009.478 2009.479 2009.483 2009.484 2009.487 2009.490 2009.491 2009.494 2009.495 2009.498 2009.499 2009.504 2009.505 2009.508 2009.511 2009.512 2009.515 2009.518 2009.519 2009.523 2009.524 2009.525 2009.526 2009.527 2009.528 2009.529 2009.530 2009.531 2009.536 2009.537 2009.542 2009.543
Kurma Kurma Lake Sarma Sarma Baikal Lake Sarma Sarma Sarma Sarma Sarma R. Khuzhir-Nuge Khuzhir-Nuge Kulura Kulura Kulura Tazhyren' Steppe Tazhyren' Steppe Listvianka Listvianka Listvianka Bol'shaia Rechka Bol'shaia Rechka Bol'shaia Rechka Karolok R. Karolok Karolok Central Park Central Park Ushakovka R. Lokomotiv Lokomotiv Irkut R. Ershi Ershi Tunka Tunka Tunka Tunka Tunka Mondy Mondy Mondy Mondy Zun-Murino Zun-Murino Tory Tory
Larix sp. n/a Pinus sp. Pinus sp. n/a Pinus sp. Pinus sp. Betula sp. Larix sp. n/a Betula sp. Larix sp. Larix sp. Larix sp. n/a Pinus sp. Pinus sp. Betula sp. Larix sp. n/a Betula sp. Pinus sp. n/a n/a Betula sp. Pinus sp. Betula sp. Larix sp. n/a Betula sp. Betula sp. n/a Betula sp. Pinus sp. Betula sp. Betula sp. Pinus sp. Pinus sp. Pinus sp. Betula sp. Betula sp. Betula sp. Larix sp. Betula sp. Pinus sp. Betula sp. Pinus sp.
larch n/a pine pine n/a pine pine birch larch n/a birch larch larch larch n/a pine pine birch larch n/a birch pine n/a n/a birch pine birch larch n/a birch birch n/a birch pine birch birch pine pine pine birch birch birch larch birch pine birch pine
listvennitsa n/a sosna sosna n/a sosna sosna bereza listvennitsa n/a bereza listvennitsa listvennitsa listvennitsa n/a sosna sosna bereza listvennitsa n/a bereza sosna n/a n/a bereza sosna bereza listvennitsa n/a bereza bereza n/a bereza sosna bereza bereza sosna sosna sosna bereza bereza bereza listvennitsa bereza sosna (kedr) bereza sosna
needles water needles needles water needles needles leaves needles water leaves needles needles needles water needles needles leaves needles water leaves needles water water leaves needles leaves needles water leaves leaves water leaves needles leaves leaves needles needles needles leaves leaves leaves needles leaves needles leaves needles
53°10'59" 53°10'59" 53°08'28" 53°08'28" 53°08'28" 53°08'36" 53°08'36" 53°06'48" 53°06'48" 53°06'48" 53°05'09" 53°05'09" 53°01'01" 53°01'01" 53°01'01" 52°53'42" 52°53'42" 51°51'38" 51°51'38" 51°51'38" 51°57'06" 51°57'06" 51°57'06" 52°08'08" 52°08'18" 52°08'18" 52°16'25" 52°16'25" 52°17'48" 52°17'15" 52°17'15" 52°17'46" 52°13'22" 52°13'22" 51°44'33" 51°44'33" 51°44'33" 51°44'33" 51°44'33" 51°41'53" 51°41'53" 51°41'53" 51°41'53" 51°44'30" 51°44'30" 51°46'30" 51°46'30"
106°58'27" 106°58'27" 106°53'32" 106°53'32" 106°53'32" 106°53'27" 106°53'27" 106°50'21" 106°50'21" 106°50'21" 106°47'24" 106°47'24" 106°44'25" 106°44'25" 106°44'25" 106°37'10" 106°37'10" 104°51'11" 104°51'11" 104°51'11" 104°44'50" 104°44'50" 104°44'50" 104°34'42" 104°34'14" 104°34'14" 104°17'40" 104°17'40" 104°17'43" 104°15'13" 104°15'13" 104°14'23" 104°19'27" 104°19'27" 102°26'01" 102°26'01" 102°26'01" 102°26'01" 102°26'01" 100°52'28" 100°52'28" 100°52'28" 100°52'28" 102°47'14" 102°47'14" 103°01'25" 103°01'25"
Table 3 Radiocarbon, and carbon and nitrogen stable isotope results for the foragers examined in this study (all tests are on human bone samples). Individuals previously analyzed by M.A. Katzenberg, University of Calgary (Fig. 4) are indicated by 1 in the column “Notes”. No.
Age, years 1418/19
Arch. age
Site
MASTER_ID
Sex
1
Manzurka
MNZ_1974.002
Undetermined
2
Borki
BO1_1971.001
Undetermined
20+
EBA
3
Obkhoi
OBK_1971.005
Undetermined
20+
EBA
4
Obkhoi
OBK_1971.007
Undetermined
20+
EBA
5
Obkhoi
OBK_1971.013
Undetermined
20+
EBA
6
Khotoruk
KHO_1977.002
Undetermined
20+
EN
7
Khotoruk Shamanskii Mys Shamanskii Mys
KHO_1978.007
Undetermined
EN
SHM_1972.002
Male Probable female
20+ 3650/55
EBA
20+
EBA
8 9
SHM_1973.003.01
EN
Sample H 1993.019 H 1993.020 H 1993.010 H 1991.015 H 1993.014 H 1993.009 H 2000.186 H 1993.003 H 1993.006
OxA
P
Date
S.d.
%Yld
%C
δ13C
δ15N
CN
25224
29751
6795
36
3.3
44.7
-19.6
10.7
3.2
1
25105
29485
3844
29
11.1
44.4
-19.6
10.9
3.2
1
25268
29767
3888
31
14.1
45.2
-19.4
10.2
3.2
1
25264
29763
4073
30
14.6
42.2
-19.0
10.8
3.2
1
25307
29770
4352
31
15.7
46.6
-19.4
11.3
3.3
1
25116
29503
6722
36
10.1
43.1
-17.2
12.7
3.2
GF
25118
29505
7657
39
10.7
44.1
-18.5
11.9
3.2
GF
Diet
25123
29510
4079
31
18.2
47.1
-19.0
14.4
3.2
GFS
24827
29513
4010
30
9.5
44.5
-18.6
16.1
3.2
GFS
Notes
Table 4 Radiocarbon, and carbon and nitrogen stable isotope results for middle Holocene foragers from the Little Sea and Upper Lena micro-regions including the results presented in Table 3 (all tests are on human bone samples). Archaeological descriptions follow (Goriunova, 1997, 2002; Goriunova and Novikov, 2010; Goriunova et al., 1998). No.
Site
Micro-region
MASTER_ID
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
Iushino Iushino Manzurka Nikol'skii Grot Nikol'skii Grot Nikol'skii Grot Zakuta Zakuta Zakuta Zakuta Borki Borki Borki Borki Borki Makarovo Manzurka Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Obkhoi Ust' Iamnaia Khotoruk Sarminskii Mys Sarminskii Mys Sarminskii Mys Sarminskii Mys Sarminskii Mys Sarminskii Mys Sarminskii Mys Sarminskii Mys Sarminskii Mys Shamanskii Mys Kulgana Shamanskii Mys Shamanskii Mys Shamanskii Mys
Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Upper Lena Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea
IUSH_2007.000.00 IUSH_2007.000.00 MNZ_1974.002 NGT_1982.001.01 NGT_1982.001.02 NGT_1982.002.01 ZAK_1993.001 ZAK_1994.002 ZAK_1994.003 ZAK_1994.005 BO1_1971.001 BO1_1971.002 BO2_1971.001 BO2_1971.002 BO2_1971.003 MKV_1973.001 MNZ_1974.001 OBK_1971.001.01 OBK_1971.001.02 OBK_1971.001.03 OBK_1971.003.01 OBK_1971.003.04 OBK_1971.004.01 OBK_1971.004.02 OBK_1971.005 OBK_1971.007 OBK_1971.010 OBK_1971.013 OBK_1971.014.01 OBK_1971.014.02 OBK_1976.003 n/a KHO_1978.005.01 SMS_1986.009 SMS_1986.011.01 SMS_1986.011.03 SMS_1986.011.04 SMS_1986.017 SMS_1986.019.01 SMS_1986.019.02 SMS_1986.019.05 SMS_1987.029.00 SHM_1976.001.01 KUL_1977.000 SHM_1972.001.01 SHM_1972.002 SHM_1973.001
Sex
Age
Archaeological age
Sample
OxA
P
Date
S.d.
%Yld
%C
δ13C
δ15N
CN
Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Undetermined Probable male Male Female Male Male Male Male Female ? Undetermined Undetermined Probable female Male Male
20+ years 20+ years 14-18/19 years 20+ years 8-13 years 20+ years Adult Adult Adult Adult 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years Subadult 20+ years 20+ years Subadult 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20+ years 20-25 years 20-50/55 years 8-18/19 years 20-50/55 years 14-18/19 years 36-56+ years 20-50/55 years ? 20+ years 20+ years 20+ years 36-50/55 years 20+ years
Early Neolithic Early Neolithic Early Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolithic Late Neolitithic Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age
H 2007.049 H 2007.049 H 1993.019 H 1992.129 H 1992.130 H 1992.131 H 1994.001 H 1994.002 H 1994.003 H 1995.001 H 1993.020 H 1993.021 H 1991.019 H 1991.026 H 1993.022 H 1991.001 H 1993.018 H 1991.008 H 1993.011 H 1991.032 H 1991.004 H 1991.006 H 1991.009 H 1991.010 H 1993.010 H 1991.015 H 1993.013 H 1993.014 H 1991.029 H 1991.030 H 1991.005 H 1997.273 H 1991.043 H 1997.001 H 1994.008 H 2000.511 H 2000.512 H 2000.515 H 2000.516 H 2000.517 H 2000.518 H 2000.521 H 1993.008 H 1991.035 H 1991.023 H 1993.003 H 1993.007
25153 25154 25224 25225 25226 25227 25498 25499 25575 25576 25105 25108 25109 25111 25110 25221 25223 25232 25269 25267 25229 25231 25262 25263 25268 25264 25270 25307 25265 25266 25230 25496 25156 25564 25563 25487 25488 25566 25567 25568 25569 25571 25127 25130 25121 25123 25126
29500 29500 29751 29753 29754 29755 30143 30144 30145 30146 29485 29486 29487 29489 29488 29748 29750 29760 29768 29766 29757 29759 29761 29762 29767 29763 29769 29770 29764 29765 29758 30141 29502 30114 30113 30119 30120 30122 30123 30124 30125 30127 29515 29518 29508 29510 29514
6928 6870 6795 5289 5214 5165 5121 5189 5136 4926 3844 3742 3813 3740 3764 4215 3938 4112 4159 3999 3950 4070 4089 4000 3888 4073 3922 4352 3977 3929 4391 3957 6901 4732 4736 4712 4747 4680 4846 4751 4781 4871 4902 4230 4045 4079 4153
40 37 36 33 33 32 30 30 32 33 29 30 29 29 29 32 29 30 28 30 30 31 29 29 31 30 28 31 30 29 32 29 38 32 33 28 29 32 33 32 33 31 33 31 31 31 32
15.5 12.8 3.3 17.3 14.6 15.1 7.8 16.2 10.1 16.7 11.1 14.1 9.2 12.5 9.5 15.4 15.4 15.6 12.0 12.0 15.8 15.4 18.0 19.8 14.1 14.6 1.7 15.7 16.3 14.5 17.1 17.4 5.2 8.9 3.7 7.8 8.1 12.7 2.9 6.7 7.2 11.6 4.0 10.8 16.9 18.2 4.5
44.3 44 44.7 46.1 45.8 45.6 44.9 44.5 42.9 43.6 44.4 44.2 45.1 44.8 45.3 43 46.1 44.3 46.6 42.9 45.1 44.7 43.5 39.1 45.2 42.2 43.8 46.6 48.3 47.4 44.8 46.1 44.6 42.8 46.3 42.9 43.9 46.7 35 44.7 43.6 42.6 45.8 43.6 47.3 47.1 45.5
-19.6 -19.7 -19.6 -20.0 -19.7 -19.8 -19.4 -19.6 -20.1 -20.0 -19.6 -18.8 -18.8 -19.4 -19.5 -19.6 -19.2 -19.4 -19.1 -18.7 -18.4 -19.4 -19.4 -19.1 -19.4 -19.0 -19.7 -19.4 -19.2 -19.0 -19.3 -19.4 -17.6 -17.2 -17.2 -18.0 -17.0 -17.4 -17.7 -18.4 -16.9 -17.1 -16.9 -19.2 -18.8 -19.0 -18.1
12.1 12.1 10.7 12.1 11.5 11.6 11.9 12.1 12.2 12.4 10.9 11.3 12.3 10.2 11.0 11.4 12.6 11.0 10.5 10.7 9.6 10.4 10.8 10.9 10.2 10.8 11.4 11.3 10.3 10.1 10.3 11.1 14.7 16.3 16.3 16.9 16.6 14.8 15.2 15.6 15.2 16.0 16.2 13.7 15.1 14.4 15.4
3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.3 3.2 3.2 3.2 3.3 3.2 3.2 3.3 3.2 3.2 3.2 3.3 3.3 3.2 3.2 3.3 3.3 3.2 3.2 3.2
Diet
Notes 1 1 3 3 3 3 3 3 3 3 3 3
3 3
3
3 3 3 3
1 GFS GFS GFS GFS GFS GFS GFS GFS GFS GFS GFS GFS GFS GFS GFS
2
48 49 50 51 52 53 54 55 56 57
Shamanskii Mys Shamanskii Mys Shamanskii Mys Sarminskii Mys Sarminskii Mys Sarminskii Mys Khotoruk Khotoruk Sarminskii Mys Sarminskii Mys
Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea Little Sea
SHM_1973.002 SHM_1973.003.01 SHM_1973.004 SMS_1986.012 SMS_1986.013 SMS_1987.033 KHO_1977.002 KHO_1978.007 SMS_1987.022 SMS_1987.021
Female Probable female Undetermined Female Probable male Female Undetermined Undetermined Probable female Female
20+ years 20+ years 20+ years 36-56+ years 20-30/35 years 36-50/55 years 20+ years 20+ years 20-30/35 years 20-30/35 years
Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Bronze Age Early Neolithic Early Neolithic Late Neolithic Early Bronze Age
H 1993.002 H 1993.006 H 1991.021 H 1997.002 H 2000.514 H 1997.006 H 1993.009 H 2000.186 H 2000.520 H 1997.004
25122 24827 25120 25565 25658 25486 25116 25118 25570 25484
29509 29513 29507 30115 30121 30118 29503 29505 30126 30116
4150 4010 4056 4092 4221 4065 6722 7657 7078 3813
30 30 30 30 31 27 36 39 38 27
13.5 9.5 11.1 7.6 13.7 8.2 10.1 10.7 2.8 13
Notes: 1. Surface find. 2. This grave was originally classified as EBA however the archaeological diagnostic charecteristics are ambiguous (Goriunova et al., 1998) and the radiocarbon date indicates LN age. 3. Individual analyzed previously by Dr. M.A. Kazenberg, University of Calgary (Weber at al., 2011).
46.2 44.5 45.1 41.2 43 43.5 43.1 44.1 44 42.9
-18.7 -18.6 -18.4 -17.5 -18.7 -18.3 -17.2 -18.5 -17.9 -19.1
15.5 16.1 15.7 15.0 15.4 13.8 12.7 11.9 12.8 12.1
3.3 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.3 3.2
GFS GFS GFS GFS GFS GFS GF GF GF GF
Table 5 Descriptive statistics for carbon and nitrogen stable isotope results for middle Holocene foragers from the Upper Lena and Little Sea (GFS diet) micro-regions. Descriptive statistic
Upper Lena, Oxford data1 δ13C
δ15N
Upper Lena, Calgary data2 δ13C
δ15N
Little Sea GF diet, Calgary data3 δ13C
δ15N
Mean
-19.4
11.2
-19.6
10.6
-19.4
11.9
Median
-19.4
11.1
-19.6
10.2
-19.4
11.9
Standard Deviation
0.38
0.78
0.47
1.00
0.29
0.63
Range
1.7
3.0
1.8
3.0
1.2
2.5
Minimum
-20.1
9.6
-20.6
9.1
-20.1
10.3
Maximum
-18.4
12.6
-18.8
12.1
-18.9
12.8
32
32
20
20
31
31
Count
Notes: 1. Based on data in Table 4. 2. Based on data in Table 8 in Weber et al. (2011) exluding results for the Early Neolithic Turuka cemetery. 3. Based on data in Table 7 in Weber et al. (2011).
Table 6 Range and variance results of micro-regional comparisons for 87Sr/86Sr data grouped by sample type. N
Minimum
Maximum
Mean
Range
Std. Error
Std. Deviation
Variance
Angara
60
.70791
.71680
.7098197
.0088940
.00017288
.00133916
1.79E-06
Little_Sea
103
.70694
.77350
.7174613
.0665570
.00116915
.01186556
1.41E-04
Tunka
121
.70673
.71812
.7101078
.0113880
.00018092
.00194856
3.80E-06
Upper Lena
79
.70814
.71191
.7096936
.0037700
.00010883
.00096731
9.36E-07
Angara
39
.70811
.71680
.7096641
.0086900
.00023565
.00147160
2.17E-06
Little_Sea
54
.70694
.74614
.7151879
.0392030
.00123168
.00905094
8.19E-05
Tunka
14
.70849
.71112
.7097906
.0026320
.00022928
.00085790
7.36E-07
Upper_Lena
17
.70881
.71097
.7094852
.0021600
.00018333
.00075589
5.71E-07
7
.70791
.71169
.7095367
.0037840
.00043510
.00115115
1.33E-06
Samples All samples
Fauna
Water Angara Little_Sea
11
.70877
.77350
.7276372
.0647260
.00610954
.02026306
4.11E-04
Tunka
26
.70711
.71373
.7096870
.0066220
.00028721
.00146450
2.14E-06
Upper Lena
16
.70814
.70917
.7085601
.0010230
.00007369
.00029476
8.69E-08
Angara
14
.70951
.71220
.7103945
.0026860
.00022765
.00085180
7.26E-07
Little_Sea
38
.70783
.75225
.7177463
.0444220
.00178740
.01101824
1.21E-04
Tunka
81
.70673
.71812
.7103102
.0113880
.00025282
.00220403
4.86E-06
Upper Lena
46
.70875
.71191
.7101648
.0031620
.00012243
.00083037
6.90E-07
Plants
Table 7 87 Sr/86Sr ratios and strontium and rubidium elemental concentrations (ppm) for water samples. Location information is grouped by cultural micro-region. Micro-Region Tunka
Little Sea
Angara
Upper Lena
87
Sr/86Sr
Sample ID
Site
Latitude
Longitude
Rb
Sr
2009.280
Belyi Irkut River
51°46'12"
100°42'33"
0.70711
0.00185
0.17456
2009.289
Aerkhan River
51°41'50"
100°52'18"
0.70733
0.00131
0.21084
2009.290
Irkut River
51°40'26"
101°01'06"
0.70862
0.00148
0.11991
2009.284
Irkut River
51°44'58"
100°43'56"
0.70878
0.00099
0.09640
2009.339
Tunka River
51°44'24"
102°31'53"
0.70878
0.00100
0.21998
2009.357
Irkut River
51°42'49"
102°25'50"
0.70882
0.00164
0.18367
2009.318
Irkut River
51°38'55"
101°41'12"
0.70882
0.00183
0.15089
2009.236
Irkut River
51°43'03"
102°35'13"
0.70886
0.00131
0.18421
2009.304
Khalagan River
51°37'56"
101°33'23"
0.70910
0.00248
0.16379
2009.311
Ekhe-Ukhgun' River
51°40'45"
101°40'46"
0.70915
0.00135
0.23228
2009.329
Kyngarga River
51°55'02"
102°25'34"
0.70918
0.00158
0.29956
2009.216
Irkut River
51°46'39"
103°14'41"
0.70927
0.00106
0.12286
2009.356
Okhor-Shibir River
51°37'55"
102°21'50"
0.70954
0.00114
0.06806
2009.325
Bol'. Zangisan River
51°40'00"
101°49'43"
0.70961
0.00152
0.12550
2009.273
Chernyi Irkut River
51°54'04"
100°45'30"
0.70964
0.00030
0.05619
2009.269
Suser River
51°54'20"
100°45'27"
0.70978
0.00032
0.03438
2009.208
Bol'. Bystraia River
51°43'51"
103°23'13"
0.70980
0.00054
0.04888
2009.297
Bol'. Khara-Gol River
51°39'16"
101°15'05"
0.70983
0.00191
0.12141
2009.229
Zun-Murino River
51°43'56"
102°51'40"
0.70990
0.00107
0.08002
2009.370
Bezymiannaia River
51°35'42"
103°54'34"
0.70996
0.00100
0.05614
2009.215
Sredniaia Tibelti River
51°45'41"
103°15'17"
0.71013
0.00049
0.04287
2009.207
Kultuchnaia River
51°44'04"
103°38'12"
0.71019
0.00035
0.20431
2009.261
Kitoi River
52°02'56"
101°05'53"
0.71070
0.00035
0.04807
2009.248
Tsagan-Ugun River
51°48'01"
102°58'31"
0.71229
0.00044
0.07211
2009.349
Ulan-Gorkhon River
51°41'23"
102°30'41"
0.71294
0.00095
0.06312
2009.265
Chernyi Irkut River
51°57'23"
100°56'35"
0.71373
0.00028
0.09218
2009.395
Bugul'deika River
52°50'27"
106°04'58"
0.70877
0.00019
0.16499
2009.443
Baikal Lake
53°01'05"
106°53'47"
0.70902
0.00054
0.10416
2009.436
Baikal Lake
53°12'11"
107°20'36"
0.70921
0.00060
0.10952
2009.396
Bugul'deika River
52°32'52"
106°03'49"
0.71271
0.00025
0.20915
2009.477
Kulura
53°01'01"
106°44'25"
0.72000
0.00025
0.14683
2009.423
Gorkhon River
52°51'01"
106°28'51"
0.72127
0.00026
0.18706
2009.450
Kurma Lake
53°10'59"
106°58'27"
0.72512
0.00033
0.07199
2009.454
Baikal Lake
53°08'28"
106°53'32"
0.73830
0.00053
0.10471
2009.464
Sarma River
53°06'48"
106°50'21"
0.73835
0.00030
0.04412
2009.403
Dolon-Bogot River
52°36'23"
106°08'42"
0.74774
0.00019
0.11023
2009.416
Anga River
52°47'57"
106°30'59"
0.77350
0.00046
0.07088
2009.175
Kuda River
52°55'56"
104°51'25"
0.70791
0.00032
1.19480
2009.487
Listvianka
51°51'38"
104°51'11"
0.70895
0.00057
0.10782
2009.495
Karolok River
52°08'08"
104°34'42"
0.70912
0.00034
0.07611
2009.515
Irkut River
52°17'46"
104°14'23"
0.70951
0.00107
0.13215
2009.508
Ushakovka River
52°17'48"
104°17'43"
0.70976
0.00023
0.11826
2009.182
Irkut River
52°04'34"
103°53'34"
0.70982
0.00095
0.12591
2009.494
Bol'shaia Rechka
51°57'06"
104°44'50"
0.71169
0.00013
0.05739
2009.099
Gul'ma River
54°12'53"
105°27'08"
0.70814
0.00023
0.90415
2009.141
Tal'ma River
54°01'12"
105°29'57"
0.70814
0.00017
0.58098
2009.142
Kulenga River
54°06'01"
105°35'05"
0.70822
0.00017
0.52288
2009.119
Kulenga River
53°40'41"
105°19'16"
0.70830
0.00014
0.36438
2009.078
Anga River
53°56'14"
106°02'49"
0.70836
0.00022
0.60654
2009.112
Magdan River
53°37'34"
105°17'56"
0.70841
0.00016
0.38179
2009.079
Tutura River
54°47'23"
105°14'06"
0.70848
0.00068
0.69863
2009.023
Khodontsa River
53°22'33"
106°00'24"
0.70851
0.00022
1.12712
2009.120
Inei River
53°43'12"
105°20'03"
0.70855
0.00014
0.28301
2009.086
Lena River
54°39'54"
105°13'05"
0.70865
0.00021
0.98206
2009.034
Manzurka River
53°43'24"
105°59'37"
0.70869
0.00033
2.31965
2009.161
Anga River
53°58'41"
106°11'15"
0.70873
0.00016
0.33658
2009.168
Lena River
53°56'38"
105°53'04"
0.70882
0.00016
1.05407
2009.057
Biriulka River
53°51'54"
106°20'18"
0.70885
0.00031
1.01801
2009.056
Lena River
53°50'22"
106°20'41"
0.70893
0.00022
0.83341
2009.055
Ilikta River
53°49'25"
106°24'54"
0.70917
0.00024
0.66226
Table 8 Provenance determinations for individual human teeth analyzed. No.
SITE
MASTER_ID
ELEMENT
HSAMP_ID
PROVENANCE
1
Manzurka
MNZ_1974.002
M1
2000.224
Upper Lena
2
Manzurka
MNZ_1974.002
M2
2000.225
Upper Lena
3
Manzurka
MNZ_1974.002
M3
2000.226
Upper Lena
4
Borki 1
BO1_1971.001
M1
2000.220
Upper Lena/Little Sea
5
Borki 1
BO1_1971.001
M2
2000.221
Upper Lena/Little Sea
6
Borki 1
BO1_1971.001
M3
2000.222
Upper Lena/Little Sea
7
Obkhoi
OBK_1971.005
M1
2000.216
Upper Lena
8
Obkhoi
OBK_1971.005
M2
2000.217
Upper Lena
9
Obkhoi
OBK_1971.005
M3
2000.218
Upper Lena
10
Obkhoi
OBK_1971.007
M1
2000.209
Upper Lena
11
Obkhoi
OBK_1971.007
M2
2000.210
Upper Lena
12
Obkhoi
OBK_1971.013
M1
2000.212
Upper Lena
13
Obkhoi
OBK_1971.013
M2
2000.213
Upper Lena
14
Obkhoi
OBK_1971.013
M3
2000.214
Upper Lena
15
Khotoruk
KHO_1977.002
M1
2000.190
Little Sea
16
Khotoruk
KHO_1977.002
M2
2000.191
Little Sea
17
Khotoruk
KHO_1977.002
M3
2000.192
Little Sea
18
Khotoruk
KHO_1978.004.01
M1
2000.194
Little Sea Little Sea
19
Khotoruk
KHO_1978.004.01
M2
2000.195
20
Khotoruk
KHO_1978.004.01
M3
2000.197
Little Sea
21
Khotoruk
KHO_1978.004.02
M1
2000.171
Little Sea
22
Khotoruk
KHO_1978.004.02
M2
2000.172
Little Sea
23
Khotoruk
KHO_1978.004.02
M3
2000.173
Little Sea
24
Khotoruk
KHO_1978.004.03
M1
2000.187
Little Sea
25
Khotoruk
KHO_1978.004.03
M2
2000.188
Little Sea
26
Khotoruk
KHO_1978.004.04
M1
2000.179
Little Sea
27
Khotoruk
KHO_1978.004.04
M2
2000.180
Little Sea
28
Khotoruk
KHO_1978.004.04
M3
2000.181
Little Sea
29
Khotoruk
KHO_1978.007
M1
2000.183
Little Sea
30
Khotoruk
KHO_1978.007
M2
2000.184
Little Sea Little Sea
31
Khotoruk
KHO_1978.007
M3
2000.185
32
Shamanskii Mys
SHM_1972.002
M1
2000.205
Little Sea
33
Shamanskii Mys
SHM_1972.002
M2
2000.206
Little Sea
34
Shamanskii Mys
SHM_1972.002
M3
2000.207
Little Sea
35
Shamanskii Mys
SHM_1973.003.01
M2
2000.198
Upper Lena
36
Shamanskii Mys
SHM_1973.003.01
M3
2000.199
Upper Lena
37
Shamanskii Mys
SHM_1975.001
M1
2000.201
Upper Lena/Little Sea
38
Shamanskii Mys
SHM_1975.001
M2
2000.202
Upper Lena/Little Sea
39
Shamanskii Mys
SHM_1975.001
M3
2000.203
Upper Lena/Little Sea
