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Geochemical composition of lithic raw materials in the Olduvai Basin, Tanzania


Egeland, Charles et al. (2021), Geochemical composition of lithic raw materials in the Olduvai Basin, Tanzania, Dryad, Dataset,


The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75–80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarsegrained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant.


A total of 244 rock specimens (aka "geological specimens") were collected from eight primary (six granulite outcrops, one gneiss outcrop, one phonolite outcrop) and one secondary (a seasonal drainage containing basalt blocks) lithic raw material sources in the Olduvai Basin. Rock specimens were flaked directly from the sources with a rockhammer. Only granulite specimens with visually quartz-rich compositions were selected. Five quartz-rich metamorphic artifacts (aka "archaeological specimens") from BK East, a ca. 1.5 million-year-old site on the south wall of the side gorge in Olduvai Gorge, were also included. Portable XRF (pXRF) analyses were conducted with an Innov-X Delta Classic Environmental Analyzer equipped with a 4W Au anode X-ray tube and a Si-PIN diode detector. All analyses were performed while the instrument was docked into a stable, hands-free test stand. An unweathered, non-cortical surface free of sediment matrix was placed over, and completely covered, the detector window. Each specimen was measured for 360 seconds using all three of the instrument's beams (120 seconds/beam). After an initial energy scale calibration test with a factory issued metal coin of known composition, the following protocol was observed: (1) a powdered sample of Standard Reference Material (SRM) 2702 with elemental concentrations certified by NIST was measured; (2) four geological/archaeological specimens were then measured; (3) the fifth geological/archaeological specimen in a series was measured five times (that is, five consecutive 360-second cycles) without being moved or reoriented; (4) after the fifth geological/archaeological specimen was measured, the SRM 2702 sample was measured once again, which initiated the next series of measurements. Element concentrations were derived with the Compton Normalization correction model and the factory-set “Soil Environmental” calibration.

Usage Notes

Variable Description

Type of sample (calibration = calibration coin for Delta Innov-X Analyzer; standard = NIST geological standard; geological = geological sample from lithic raw material source; artifact = archaeological specimen)


Replicate measurement (Yes or No)


Geological source (delta = Delta Innov-x Analyzer calibration coin; nist = National Institute of Standards and Technology (NIST) geological standard; NS = Naibor Soit; NH = Naisuisui Hill; OL = Oldonye Okule; LD = Lemagarut drainage; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin)


Individual outcrop within geological source (NSM = Naibor Soit Main Hill; NSMH = Naibor Soit Manyata Hill; NSSO = Naibor Soit Southern Outlier; NH = Naisuisui Hill; LD1 = Lemagarut Drainage 1; LD2 = Lemagarut Drainage 2; BKE = BK East; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin; NA = Not applicable)


Individual find or sample number


Raw material type (QTZ = "Quartz-rich"; GN = Gneiss; FGV = Fine-grained volcanic)

Element concentration estimate Reported for each element (e.g., P, Cl, Ca; empty cells are "non-detect")
Analytical error Reported for each element (e.g., P +/-, Cl +/-, Ca +/-; no error reported for "non-detect" elements)

The published analysis focused only on granulite specimens (n = 186) and, more specifically, on six elements (Fe, Ti, Zr, K, Sr, and Y) that had detection rates >75% in the granulite specimens (that is, these elements were detected in more than 75% of the granulite specimens). These elements were used in the predictive models from the published analysis. Two of the 186 granulite specimens were missing values for five out of the six elements and were therefore not included in the statistical analyses. Of the remaining 184 specimens, 55 had missing data for one element, and two of those 55 had missing data for two elements. These missing values were treated as censored data (that is, the element is present but could not be measured precisely enough for the instrument to report a value). These missing values were interpolated in one of two ways. For those specimens subjected to replicate pXRF runs (n = 7), the missing value was replaced with the mean value of the replicates. The missing values for the remaining specimens (n = 48) were replaced with the mean of the four closest (as determined in two-dimensional space) specimens with measured (rather than interpolated) values. The main data file does not include these interpolated values. Should analysts choose to use them, interpolated values can be found in the additional .csv file.


Wenner-Gren Foundation

University of North Carolina at Greensboro

Borman Foundation

Earlham College