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Journal of Paleolithic Archaeology

, Volume 2, Issue 1, pp 74–97 | Cite as

Evolution of Silcrete Heat Treatment in Australia—a Regional Pattern on the South-East Coast and Its Evolution over the Last 25 ka

  • Patrick SchmidtEmail author
  • Peter Hiscock
Article

Abstract

We document long-term evolution in the rate of heat treatment in Eastern Australia and explore them as a technological response to dynamic industrial and social contexts that developed in the last 25 millennia. We employed methods previously used in Africa but novel in Australia to infer long-term directional changes in the relative frequency of silcrete artefacts that were heat-treated. Our methods involved independent and cross-verifying tests of the presence or absence of heat treatment, employing visual classifications and surface roughness measures. These methods revealed a coherent series of increases over time in the landscapes around Sydney, so that Late Holocene assemblages displayed higher rates of heat treatment than terminal Pleistocene or Early Holocene ones. We hypothesise that the directional trend towards greater frequencies of heat-treated artefacts on Australia’s eastern seaboard is explicable in terms of the context of technological shifts towards microlith production superimposed onto an even longer term process of lithic resource depletion, perhaps compounded with the development of political barriers to the redistribution of knappable stone.

Keywords

Early transformative technology Stone heat treatment Lithic technology Raw material transformation Fracture surface measurements 

Introduction

Australia belongs to a continent colonised by behaviourally modern humans 60–70 ka ago (Clarkson et al. 2017). The colonists were highly adaptable foragers whose ancestors had dispersed through many different environments after emerging from Africa. Their technology was transformed multiple times on this journey, and the materials that they used to make artefacts varied between regions. Multiple adjustments of technology to local conditions displayed on route to Australia were required as foragers colonised the different environments, each with different raw materials for artefact manufacture. We know that heat treatment of lithic material, to improve flaking characteristics, has a long history in Africa (Brown et al. 2009). There is currently no evidence of heat treatment in groups of early foragers dispersing from Africa to the antipodes, but heat treatment is known to have been used by the populations that occupied Pleistocene Australia (see for example: Hanckel 1985). In Australia, heat treatment began being documented in the 1970s (Akerman 1979), and there are now numerous regions in which the procedure is reported to have been employed, including the Kimberley in the northwest (Akerman 1979), central Queensland in the north east (Cochrane et al. 2012), and Tasmania in the southeast (Flenniken and White 1983). Inter-regional variation in the methods of heat treatment may well exist but have not yet been documented. Similarly, there is no coherent record of chronological change in the frequency or nature of treatment, with the vast majority of reported sites being either undated (Flenniken and White 1983; Akerman 1979; Rowney 1994) or Late Holocene in antiquity (Rowney 1994; McDonald and Rich 1994; Cochrane et al. 2012).

In this paper, we study the evolution of heat treatment, by which we mean shifts in the frequency of this process in technological systems. We do this by examining temporal changes in heat treatment in Southeastern Australia to reveal the way technological behaviours evolved to suit local conditions and niche change. Heat-treating behaviours in Australia can readily be studied as many assemblages are rich in silcrete, a flakeable rock that typically displays heat treatment well (see for example: Schmidt et al. 2013). To establish a baseline framework for geographical and chronological changes in Australian silcrete heat treatment, we present a study of selected sites on the eastern seaboard, centred in landscapes around Sydney (Fig. 1). Heat treatment has previously been reported in the Sydney Basin and the Hunter River Valley (Corkill 1997; Hanckel 1985; Rowney 1994; McDonald and Rich 1994; Hiscock 1993b). The oldest secure evidence of heat treatment in Australia is currently dated to approximately 25,000 years BP at the site of Burrill Lake, a cavernous rock shelter in the southern part of the Sydney Basin (Hanckel 1985). The stratified sequence contained in the Burrill Lake deposit was used by Hanckel (1985) to show that silcrete heat treatment had been used in all or most of the strata, covering the last 20 millennia, but his destructive methods did not allow him to obtain a sample of artefacts throughout the sequence and hence he was unable to determine if there were any evolutionary trends in the use of the heating procedure. However, Hanckel’s study revealed the potential of the Burrill Lake sequence for explorations of evolutionary shifts in heat treatment.
Fig. 1

Location of the three studied sites on Australia’s south-east coast. Origins of geological silcrete reference samples used in this study are marked X. The first two letters of silcrete sample names refer to the names on the abscissa in Fig. 4b

Across the Sydney and Hunter regions, there are many sites with assemblages containing silcrete and a number have sequences, providing samples suited for investigations of temporal change in silcrete use and heat treatment. Both regions have been subjected to intense archaeological investigations for decades and there is evidence of significant climatic shifts, such as the onset/intensification of ENSO during the Holocene. Archaeological evidence for altered patterns of land use (including raw material supply) and major transformations of lithic technology have been correlated to those climatic shifts and it has been suggested that technological changes were responses to altered resource availability and predictability (Attenbrow et al. 2009; Hiscock 2006, 2018; Hiscock 1994). The most intensely discussed change was a dramatic increase in the production rates of microlithic backed artefacts. They appear in the local record at the beginning of the Holocene and there was a period beginning approximately 3.5 ka BP in which these microliths were produced at high rates. This ‘proliferation’ period saw microliths become the dominant form of retouched flake. This efflorescence of microlith production has been associated with altered raw material procurement and core reduction procedures, though the nature of the alterations is the subject of debate (see Hiscock 2008). Hence, we know the lithic strategies of foragers inhabiting the southeastern seaboard of Australia evolved significantly over the last 20–30 ka BP. This knowledge frames the central question of our paper: what, if any, evolutionary changes occurred in the practice of heat treatment? Our answers to this question should illuminate the articulation of heat treatment to reduction technology in southeastern Australia.

Methods and Materials

Silcretes in the Studied Assemblages and Their Potential Response to Heat Treatment

Silcrete is a continental silica rock resulting from the concretion of sediments by a secondary quartz cement (Summerfield 1983b). It therefore consists of two generations of silica: quartz clasts inherited from the sediment and the quartz cement, most often micro-quartz (Summerfield 1983a), that precipitated around those clasts. Silcrete is normally separated into two categories, groundwater silcrete formed by the precipitation of the cement in a more or less stagnant underground water table (Thiry 1991) and pedogenic silcrete formed by the crystallisation of quartz in weathering profiles (Nash and Ullyott 2007). Before analysing whether archaeological silcrete assemblages contain intentionally heated artefacts, assessing what silcrete types are present may allow a first evaluation of whether it is likely to find heat treatment or not. This is because both types of silcrete have been reported to behave differently during heat treatment, ground water silcrete showing no apparent improvement and pedogenic silcrete drastically improving in quality for tool knapping (Schmidt et al. 2017a, b). The two silcrete types are documented in Australia (Thiry 1991; Stephens 1971; Summerfield 1983c) and it may in some cases be difficult to distinguish both macroscopically (Ullyott et al. 1998; Taylor and Eggleton 2017). Working on the archaeological collections in this study, we observed numerous markers of illuviation (for a description see: Summerfield 1983a) on almost all silcrete artefacts, suggesting that they were made from pedogenic silcrete. We also noted the presence of all known silcrete texture types, ranging from very fine matrix-supported silcretes to rather coarse grain-supported rocks. Thus, there is no reason to expect that the silcretes we analysed in our study would behave differently during heating than the African Cape silcretes that have been described by several previous heat treatment studies (Schmidt et al. 2017a; Schmidt et al. 2013).

Geological Samples and Heating Experiment

To verify whether heat-treated silcrete from the study area can be visually distinguished from unheated silcrete, the same way this can be done with African Cape silcrete (see for example: Schmidt 2017), we conducted fieldwork in the Hunter Valley, north of Sydney, collecting five macroscopically different silcrete cobbles from the Hunter stream bead. One more sample from near Bendalong, about 250 km to the south-west, was kindly provided by a colleague (for sample localities see Fig. 1). These reference samples macroscopically reflect the variability of studied archaeological silcretes, ranging from fine matrix-supported to coarse grain-supported rocks. A flake was stuck from each of these six samples with a hammer-stone. The remaining samples were heated to 400 °C for 2 h in an electrical furnace (for justification of these heating parameters see: Schmidt et al. 2017a; Schmidt et al. 2016). After cooling to room temperature, a second flake was removed from each sample. The so produced 12 flakes (six heated + six unheated) were analysed along artefacts, using the replica tape method described below.

Archaeological Samples

Our analysis required large assemblages of silcrete artefacts from contexts with dates and if possible sequences. In this paper, we report the analysis of three such sites. Burrill Lake Rockshelter, a large cavernously weathered shelter in the south of the study area excavated by Ron Lampert in the 1960s (Lampert 1971), yielded well-stratified deposits. Shaw’s Creek rockshelter is a smaller cavernous hollow on the edge of the Blue Mountains, near the Nepean River west of Sydney. It was excavated by Eugene Stockton in the early 1970s and although the deposit is often discussed as having low chronological resolution because of vertical displacement of artefacts, the frequency and magnitude of displacement in this deposit have never been formally assessed (Stockton 1973; Stockton and Holland 1974). A nearby shelter, Shaws Creek K2, has also been dug but that assemblage was not available for this study. Our third sample came from Bulga, an unpublished site excavated by Margrit Koettig that is positioned on the southern margins of the Hunter River Valley. These sites were selected to provide samples from different portions of the study area (Fig. 1), and from different landscape contexts.

Burrill Lake Rock Shelter contains a substantial and relatively well-dated sequence. Lampert (1971) obtained five radiocarbon dates from four of the layers (Table 1). The chronology Lampert constructed was broadly confirmed by the re-dating of a fossil plunge pool at the dripline to about 4–5000 ka (Hughes (1977). The sequence spanned from ~ 25 ka to historical times and this is the key sequence in our study. Shaw’s Creek K1 on the other hand was never dated with radiocarbon and we rely for relative chronology on six phases in the stratigraphic sequence described by Stockton. Phases I and II are recent. Phases III and IV contain distinctive microliths which in the Sydney Basin have been shown to be produced in large numbers between 3.5 and 1.5 ka (Hiscock 2008, 2018; Hiscock and Attenbrow 1998). Stockton described Phase V as representing the beginning of microlithic production, which at other sites in the region would be the early- to mid-Holocene (Hiscock and Attenbrow 1998, 2004). The earliest part of the sequence, labelled as Phase VI, was described by Stockton as having yielded heavy artefacts without microliths, which would be consistent with an early Holocene age or older. This sequence is comparable to that published for the Shaw’s Creek K2 Shelter (Kohen et al. 1981; Kohen et al. 1984) and the Capertee 3 shelter (McCarthy 1964). The Bulga B46 site is undated but is comparable to other sites at Bulga which date to the last two millennia (Koettig 1994). We therefore place the Bulga B46 material as Late Holocene. We place these three sites in a common chronological structure, based on the unambiguous radiometric dating of Burrill Lake, combined with the well-defined sequence from Shaw’s Creek, which is articulated with the Burrill lake sequence through association of the region-wide and well-dated period of high levels of microlithic production. The Bulga B46 assemblage provides a data point for the Late Holocene period of microlith production. These sites are compiled into their sequence of temporal change in Table 1.
Table 1

Analysed assemblages and their age estimation. All age estimates in calibrated years

Approximate age

Burrill Lake layer

Burrill Lake C14[yrs. BP]

Shaw’s Creek KI

Bulga B46

0–1.5 ka

Level I

< 1.5 ka

Phases I–II

 

1.5–6 ka

Level II

1565 ± 101, 6100 ± 156

Phase III, Phase IV

Main layer

6–16 ka

Level III

14,696 ± 414

Phase V

 

16–25

Level IV, Level V

24,990 ± 1063

24,898 ± 1043

Phase VI

 
For each of the sites, we defined a sample that was consistent with the character of the deposit and assemblage. For Burrill Lake, we sought at least 150 silcrete artefacts from each of the archaeological levels defined by Lampert (1971) to facilitate statistical analyses. Specimens from Burrill Lake are not individually numbered and multiple specimens from the same excavation level are bagged together. In this situation our sampling decision was that if the last opened bag contained more silcrete pieces than necessary to attain the needed 150 artefacts, we recorded all specimens in this bag rather than create unnecessary selection by sub-sampling; therefore, the number of analysed artefacts from each layer is slightly higher than 150. For Shaw’s Creek, we analysed all silcrete pieces from Stockton’s excavations. For Bulga B46, the assemblage was too large to measure in its entirety and so we took a random selection of 200 silcrete artefacts for analysis. These decisions yielded a total sample of 1392 silcrete artefacts: 788 from Burrill Lake, 404 from Shaw’s Creek KI and 200 from Bulga B46. Figure 2 displays examples of these artefacts. All these artefacts were visually classified into heating proxy categories using the protocol described below.
Fig. 2

Heat-treated and not-heated lithics from Burrill Lake, Shaw’s Creek and Bulga B46. Burrill Lake: a, d and e and h, j and k; Shaw’s Creek: b and c and i and l; Bulga B46: f and g. Pieces a and h are made of the same type of silcrete and the different fracture patterns can be appreciated comparing both pieces

Visual Classification of Heating Proxies

We applied a protocol that was initially proposed by Schmidt et al. (2015) and subsequently applied in several other studies on heat treatment in the South African Middle and Later Stone Age (MSA and LSA) (Delagnes et al. 2016; Porraz et al. 2016; Schmidt and Mackay 2016). Two proxies were used for visual classification: (1) Pre-heating removal scars: relatively rough fracture surfaces corresponding to the removal of flakes from unheated silcrete (Fig. 2g–l) and (2) Post-heating removal scars: relatively smooth fracture surfaces that correspond to the removal of flakes from heat-treated silcrete (Fig. 2a–f). In some previous works (Delagnes et al. 2016; Schmidt et al. 2015) both heating proxies were identified on artefacts through a piece-by-piece comparison with an experimental (external) reference collection. Here, the assignment to different heating proxy categories was solely based on an “internal calibration” (Schmidt 2017; Schmidt and Mackay 2016): first, artefacts made from different silcrete types, that show clearly distinguishable roughness contrasts between adjacent scars on their dorsal side, were selected. Such pieces (Fig. 3) are considered ‘diagnostic’ because the roughness difference between two adjacent scars on one side of a single piece, when the smooth scar is later than the rough scar, cannot be explained by different silcrete structure, inner sample heterogeneity or taphonomy. We note that other mechanisms such as natural wild fires or accidental exposure to hearths are not able to explain the archaeological specimens studied here; neither the stone sources of the stratigraphic position within the rockshelters is consistent with such mechanisms. Only one explanation of this pattern is left: rough pre- and smoother post-heating removal scars result from knapping before and after heat treatment respectively. Hence, diagnostic pieces document a stage of pre-heating knapping, the transformation of their fracture mechanics (heat treatment) and a second stage of post-heating knapping in a single artefact. Such pieces have consistently been used to identify heat treatment in assemblages since the beginning of archaeological research on heat treatment (see for example: Bordes 1969; Inizan et al. 1976; Inizan and Tixier 2001; Binder 1984; Binder and Gassin 1988; Léa 2004; Léa 2005; Terradas and Gibaja 2001; Mandeville 1973; Marchand 2001; Mourre et al. 2010; Tiffagom 1998; Wilke et al. 1991) and no alternative mechanism taking place in silica rocks (chert or silcrete) is likely to cause a comparable pattern. In light of these theoretical considerations and the acceptance of pieces with differential scar roughness in the archaeological community, we hypothesise that they unambiguously result from heat treatment. However, to evaluate our proposition, we also undertook laboratory experiments (described below in the section “Surface Roughness Measurements with Replica Tape”) on the experimental silcrete samples described above. Should the results obtained by this analysis be comparable to our measurement on diagnostic artefacts, in terms of absolute roughness and the magnitude of change between re- and post-heating scars, it may be concluded that the surface differences observed on silcrete from the study area correspond to the same mechanism described above. In other words, our identification of diagnostic specimens as a sequence of pre- and post-heating scars is accurate. They would validly provide the basis for differentiating specimens without both pre- and post-heating scars.
Fig. 3

Close-up view of roughness contrast on diagnostic pieces. The placement on the pieces of the three lower row macro-photos corresponds to the red rectangle. Note the rougher pre-heating surfaces (on the left in the close-up photos) that are cross-cut by smoother post-heating scars (on the right). a: unnumbered pieces from Level II at Burrill Lake; b: unnumbered pieces from Bulga B46; c: unnumbered pieces from Phase III at Shaw’s Creek

In practice, a visual analysis of archaeological assemblages, using an internal calibration on diagnostic pieces, means that a set of diagnostic artefacts is laid out on a large table and all other undiagnostic artefacts were compared with the pre- and post-heating scars on these diagnostic pieces. Artefacts that cannot be clearly identified as belonging to one of the frequently occurring silcrete types (for which no diagnostic comparisons could be identified) are left indeterminate in the study; this was the case of 4% of all silcrete artefacts from Burrill Lake, n = 29; 7% artefacts from Shaw’s Creek, n = 30 and 6% artefacts from Bulga B46, n = 12.

Surface Roughness Measurements with Replica Tape

To estimate the quality of this visual classification of heat treatment proxies, we conducted a quantitative fracture surface analysis using replica tape and 3D surface mapping. This technique was recently introduced for identifying archaeological heat-treated silcrete (Schmidt 2017) and is used here because it provides an independent measure of the quality of our visual identification of heat-treated silcrete in at least one of the three analysed sites. If similar counts of heat-treated vs not-heated artefact result from both approaches, it may be concluded that the visual identification of heating proxies we performed on all 1392 artefacts is likely to be trustworthy and that the analyses elaborated on southern African silcrete (Delagnes et al. 2016; Porraz et al. 2016; Schmidt and Mackay 2016; Schmidt et al. 2015) are also applicable to Australian silcrete. The replica tape method is described in detail in Schmidt (2017) and only the details absolutely necessary for its understanding are repeated here. For this analysis, a layer of compressible foam, the replica tape, is applied with force to the surface to be measured, replicating the surface irreversibly by creating a negative of it. The so-produced surface negative is scanned by light transmission, producing a 3D map of the surface.

Protocol for the Surface Roughness Measurements

Archaeological samples

Both visual and replica tape analyses were conducted by the same person (PS), first the visual classification, then, approximately 1 month later, the replica tape analysis. Replica tape surface measurements are based on a comparison between known pre- and post-heating removal scars and the removal scars on samples with unknown heating-history (test group). As for visual identification, we used an internal calibration on diagnostic pieces to provide threshold roughness values of pre- and post-heating surfaces. For this, 16 diagnostic pieces, large enough for measurement on both their pre- and post-heating surface and representing the macroscopic variability of silcrete types in the Layer III assemblage of Burrill Lake, were chosen to provide 32 values of both pre- and post-heating surfaces. Then, a random 100 flakes that had not been analysed during visual classification, were chosen as test samples. Because these samples were picked randomly (no visual identification of heating proxies), some samples were expected to be diagnostic samples. To avoid including pre-heating scars on heat-treated diagnostic pieces, we only made replica tape measurements on ventral surfaces of flakes, as these normally are the most recent removal surface.

Reference samples

To verify whether the roughness on fracture surfaces produced after heating is measurably lower than on unheated reference surfaces, we also analysed the 12 references samples with replica tape. In case such roughness difference can be detected using the replica tape method, these measurements also provide an idea of the heat-induced magnitude of change, allowing to evaluate whether the magnitude of change recorded on artefacts is similar or not.

Instruments and Data Treatment

To produce the 3D surface maps, a DeFelsko PosiTector RTR-P tape reader was used in combination with optical grade Testex PRESS-O-FILM replica tape of the grades Coarse and X-Coarse. The 3.8 × 3.8 mm wide maps resulting from these measurements were exported as surface data files and processed using the Gwyddion free software package. We extracted two statistical quantities from the 3D surface maps: the mean roughness (Ra) in micrometre and the dimensionless differential entropy S of the height value distribution (or Shannon differential entropy, Shannon 1948). The Ra value expresses the mean height difference between peaks and valleys on the surface and S expresses the randomness of the distribution of different height values recorded on the peak- and valley-surface. No software filtering was applied to extract Ra and S values. As proposed by Schmidt (2017), Ra values were transformed to their natural logarithm, so that the data can be fitted with a linear function. Because both values, Ln(Ra) and S, are so highly correlated, the variance between samples is one-dimensional and lies on this fitted function. The quality of these data can be visually estimated from the straying of data plots around the fitted function. The resulting plots allow to identify heated and unheated samples as follows: pre- and post-heating surfaces on the 16 diagnostic pieces produced 32 calibration values setting the ranges where unknown samples are expected to plot depending on whether they are not heated or heated (Fig. 5a). When analysing the 100 unknown samples, and plotting their data onto these ‘reference values’ (Fig. 5b), the following distribution will appear: some of the values will plot within the range of the 16 pre-heating surface values and others onto the range of the 16 post-heating values. It can then be estimated that every test sample plotting to the lower left of the lowest-left value of the unheated reference population is heated (i.e. during calibration with 32 known pre- and post-HT surfaces, no unheated surface plotted so low-left). Test samples plotting to the upper-right of the topmost-right heated specimen are unheated (during calibration with 32 known pre- and post-HT surfaces, no heated surface plotted so up-right). The zone in between these points is the zone where both heated and unheated samples plot, so all test samples plotting there are indeterminate.

The plots also allow for automated detection of heat-treated and not-heated samples within the test group: Ln(Ra) and S values are orthogonally projected onto this function (Schmidt 2017) and threshold Ln(Ra) values of heated and not-heated samples are defined as: all test samples with Ln(Ra) > the highest Ln(Ra) of the post-heating training values = not-heated; all test samples with Ln(Ra) < the lowest Ln(Ra) of the pre-heating training values = heat-treated and all test sample plotting between both thresholds = indeterminate. For reference samples, only Ra was extracted from the tape readings.

Results

Visual Classification of Heating Proxies

We identified both heat treatment proxies described in the section “Visual Classification of Heating Proxies” on the studied assemblages. Except for 71 artefacts (5.3%), on which the removal scars could not be clearly assigned to either pre- or post-heating removals, all artefacts from the three sites could be unambiguously separated into distinct groups using the visual identification protocol. The total count and the relative percentages of heat-treated and not-heated artefacts are summarised in Table 2 for Burrill Lake, Table 3 for Shaw’s Creek and Table 4 for Bulga B46.
Table 2

Relative prevalence of heat-treated silcrete in Burrill Lake as determined by the visual determination of heating proxies. Values under ‘n’ are piece counts and values under ‘%’ are percentages calculated to the base of all analysed pieces (values in brackets in the line ‘Diagnostic pieces’ are calculated to the base of all heat-treated pieces)

 

All levels

Level I

Level II

Level III

Level IV

Level V

n

%

n

%

n

%

n

%

n

%

n

%

Analysed pieces

788

 

161

 

168

 

181

 

166

 

112

 

Heat-treated

488

61.9

122

75.8

116

69

112

61.9

82

49.4

56

50

Not-heated

300

38.1

39

24.2

52

31

69

38.1

84

50.6

56

50

Diagnostic pieces

122

15.5 (25)

26

16.1 (21.3)

30

17.6 (25.9)

38

21 (33.9)

17

10.2 (20.7)

11

9.8 (19.6)

Table 3

Relative prevalence of heat-treated silcrete in Shaw’s Creek KI site as determined by the visual determination of heating proxies. Values under ‘n’ are piece counts and values under ‘%’ are percentages calculated to the base of all analysed pieces (values in brackets in the line ‘Diagnostic pieces’ are calculated to the base of all heat-treated pieces)

 

All Phases

Phase III

Phase IV

Phase V

Phase VI

n

%

n

%

n

%

n

%

n

%

Analysed pieces

404

 

21

 

259

 

96

 

28

 

Heat-treated

283

70

15

71.4

181

69.9

69

71.9

18

64.3

Not-heated

121

30

6

28.6

78

30.1

27

28.1

10

35.7

Diagnostic pieces

79

19.6 (27.9)

2

9.5 (13.3)

53

20.5 (29.3)

20

20.8 (29)

4

14.3 (22.2)

Table 4

Relative prevalence of heat-treated silcrete in Bulga B46as determined by the visual determination of heating proxies. Values under ‘n’ are piece counts and values under ‘%’ are percentages calculated to the base of all analysed pieces (values in brackets in the line ‘Diagnostic pieces’ are calculated to the base of all heat-treated pieces)

 

n

%

Analysed pieces

200

 

Heat-treated

172

86

Not-heated

28

14

Diagnostic pieces

46

23 (27.7)

In Burrill Lake, a trend of increasing relative prevalence of heat treatment can be observed throughout the site’s stratigraphic sequence. In both lowest layers, 49 and 50% of all silcrete were knapped after heat treatment. Throughout the younger layers, this number progressively increases up to 76% (Table 2). The two lowest lavers that yielded similar numbers of heated artefacts are both dated to around 25 ka BP (Lampert 1971), indicating that the observed trend in the heat treatment prevalence is indeed time-dependent. Statistical tests of the chronological differences in the abundance of heat treatment reveal they are significant and that chronology strongly explains heat treatment abundance (r2 = 0.94, p = 0.006 for linear regression; r2 = 0.96, p = 0.48 for quadratic regression; rs = − 0.90, p = 0.037 for Spearman’s rank relationship). We also conducted an examination of the breakage of flakes to check that greater abundance of heat treatment later in the Burrill Lake sequence was not a product of increased breakage rates. There is no significant difference in flake breakage between older and younger layers and therefore there is no trend in flake breakage rates that could underpin the long-term changes in heat treatment (χ2 = 0.139, d.f. = 2, p = 0.933 calculated for complete:broken ratio). Hence we conclude that Burrill Lake displays an evolutionary trend towards a greater employment of heat treatment in flake production.

In Shaw’s Creek, only phases IV and V yielded a sufficient number of silcrete artefacts to produce a reliable statement of the proportion of heat-treated pieces. In both phases, 70 and 72% of all silcrete artefacts were heated. This is in accordance with the trend observed at Burrill Lake as we have estimated both phases to be Mid- or Late Holocene (Table 1). At Bulga B46, a higher number of pieces was heated (86%). This result conforms to the Burrill Lake pattern, in which the majority of all silcrete flakes were heat-treated in Late Holocene assemblages.

Surface Roughness Measurements

Figure 4a is a plot of Ra values measured from the 16 diagnostic pieces that provided reference values for pre- and post-heating surfaces. Ra decreases from pre- to post-heating surfaces in all cases, confirming that the visual difference observed when choosing these diagnostic pieces indeed corresponds to differences in fracture pattern. However, one sample (sample 8) displays only a minor magnitude of change between its pre- and post-heating scars. This might be explained by silcrete type (it has recently been shown that some silcrete types only display minor roughness reduction on post-heating fracture surfaces: Schmidt et al. 2018). Figure 4b is a plot of the Ra data measured on six unheated and experimentally heated silcrete flakes. The Ra value is reduced from pre- to post-heating scars in all cases. The absolute Ra values of pre- and post-heating fracture surfaces and magnitude of Ra loss from unheated to heated are similar to the measurements made on artefacts. Figure 5a is a plot of the entropy S and logarithm of the mean roughness Ln(Ra) values of the 32 training values obtained from these 16 diagnostic pieces. These data can be well (R2 = 0.98) fitted with a linear function with S = 0.843Ln(Ra)–11.898, as ought to be the case. Pre-heating surfaces also have both higher S values than post-heating surfaces so that both groups can be comfortably separated at the two extremities of the scatter plot. However, a small overlap zone (between the black lines of Fig. 5) is present in the centre of the scatter plot, where both pre- and post-heating reference values are similar. After orthogonal projection, the lower boundary of this overlap zone can be set to Ln(Ra) = 2.19, its upper value to Ln(Ra) = 2.22. Figure 5b is a plot of the 100 test samples onto the function obtained by fitting the reference data and the overlap zone. With reference to this scatter plot, we define three categories: Heat-treated with values of Ln(Ra) < 2.19, Indeterminate with values > 2.19 and < 2.22 and Untreated with values > 2.22. Based on this classification, most of our test samples can be unambiguously assigned to either heat-treated or not-heated. Deviation of test data away from the fitted function is relatively low, indicating a good overall quality of the data. The test samples’ Ra and S values before and after orthogonal projection and the results of the assignment of these values to the three classes of heat-treated, indeterminate and not-heated are summarised in Table 5. After our classification, four pieces (4%) remain in indeterminate, 60 are found to be heat-treated and 36 not-heated. If the four indeterminate are left out, this represents 62.5% heat-treated and 37.5% not-heated (Table 6).
Fig. 4

Plots of the mean roughness values (Ra) of calibration samples from BurriIl Lake Level III with roughness contrast (a) and experimental reference samples (b). Grey squares are measurements taken on the samples’ pre-heating scars and black round dots are measurements taken on the samples’ post-heating scars. Sample numbers in (a) are random from 1 to 16 because individual pieces are not numbered in the Burrill Lake collection. Note that for all cases, post-heating scars are smoother than pre-heating scars. Also note that absolute Ra values and the magnitude of change between pre- and post-heating scars are similar in archaeological and reference samples

Fig. 5

Plots of the entropy S and mean roughness Ra values of archaeological samples from BurriIl Lake Level III. a Plot of 32 known pre- and post-heating surfaces on diagnostic artefacts and their linear best fit. The two black parallel lines mark the overlap zone in which both pre- and post-heating scar plots. The lower and upper boundaries of the overlap zone [in Ln(Ra) at the intersection of the fitted function] are 2.19 and 2.22. Note that both pre- and post-heating surfaces can be distinguished at the two extremities of the scatter plot. b Plot of 100 archaeological test samples onto the reference function and indet. zone of a

Table 5

Results of the Replica tape measurements of 100 archaeological test samples from Level III at Burrill Lake. Sample numbers are random from 1 to 100 because individual pieces are not numbered in the Burrill Lake collection. Roughness Ra and Entropy S values on the left are measured on archaeological test samples, Ln(Ra) values are calculated from the measured data. Values under ‘x on function [Ln(Ra)]’ are perpendicular projections of Ln(Ra) onto the function derived by fitting the data obtained from known archaeological pre- and post-HT scars on diagnostic pieces (S = 0.843Ln(Ra)–11.898). Values under ‘y on function [S]’ are the corresponding y coordinates on the fitted functions. Entries under ‘Result’ are obtained from the position of the projected values (their one-dimensional variance) with respect to the overlap zone’s position on these fitted functions. Thresholds are defined by the overlap zone in reference scatter plots and are [in Ln(Ra)]: Heated < 2.19, Indet. > 2.19 < 2.22, Not-heated > 2.22

Random sample no

Ra[μm]

Ln(Ra)

S

x on function [Ln(Ra)]

y on function [S]

Result

1

9.33

2.233

− 9.98

2.248

− 10.00

Not-heated

2

9.16

2.215

− 10.02

2.220

− 10.03

Indet.

3

7.16

1.969

− 10.25

1.963

− 10.24

Heated

4

7.41

2.003

− 10.16

2.027

− 10.19

Heated

5

8.16

2.099

− 10.13

2.098

− 10.13

Heated

6

5.75

1.750

− 10.45

1.736

− 10.43

Heated

7

10.74

2.374

− 9.91

2.369

− 9.90

Not-heated

8

19.61

2.976

− 9.41

2.966

− 9.40

Not-heated

9

8.17

2.100

− 10.10

2.114

− 10.12

Heated

10

7.14

1.966

− 10.21

1.981

− 10.23

Heated

11

8.16

2.099

− 10.14

2.093

− 10.13

Heated

12

7.21

1.975

− 10.24

1.971

− 10.24

Heated

13

5.29

1.666

− 10.52

1.653

− 10.50

Heated

14

7.16

1.969

− 10.25

1.963

− 10.24

Heated

15

12.56

2.531

− 9.75

2.538

− 9.76

Not-heated

16

10.77

2.377

− 9.96

2.344

− 9.92

Not-heated

17

11.24

2.419

− 9.85

2.424

− 9.85

Not-heated

18

6.74

1.908

− 10.33

1.888

− 10.31

Heated

19

12.02

2.487

− 9.84

2.468

− 9.82

Not-heated

20

7.16

1.969

− 10.23

1.973

− 10.23

Heated

21

5.52

1.709

− 10.50

1.688

− 10.47

Heated

22

8.31

2.117

− 10.09

2.129

− 10.10

Heated

23

6.13

1.813

− 10.38

1.808

− 10.37

Heated

24

6.23

1.830

− 10.37

1.822

− 10.36

Heated

25

7.99

2.078

− 10.13

2.086

− 10.14

Heated

26

8.04

2.084

− 10.19

2.060

− 10.16

Heated

27

9.23

2.222

− 9.97

2.248

− 10.00

Not-heated

28

8.85

2.180

− 10.05

2.185

− 10.06

Heated

29

11.56

2.448

− 9.85

2.441

− 9.84

Not-heated

30

13.19

2.579

− 9.68

2.599

− 9.71

Not-heated

31

7.27

1.984

− 10.22

1.987

− 10.22

Heated

32

11.10

2.407

− 9.81

2.437

− 9.84

Not-heated

33

7.89

2.066

− 10.13

2.079

− 10.15

Heated

34

8.79

2.174

− 10.06

2.176

− 10.06

Heated

35

9.84

2.286

− 9.94

2.301

− 9.96

Not-heated

36

9.99

2.302

− 9.92

2.319

− 9.94

Not-heated

37

9.55

2.257

− 9.97

2.268

− 9.99

Not-heated

38

8.51

2.141

− 10.09

2.142

− 10.09

Heated

39

7.99

2.078

− 10.11

2.096

− 10.13

Heated

40

11.78

2.466

− 9.82

2.464

− 9.82

Not-heated

41

6.85

1.925

− 10.25

1.937

− 10.26

Heated

42

6.96

1.940

− 10.26

1.941

− 10.26

Heated

43

8.41

2.129

− 10.10

2.131

− 10.10

Heated

44

5.72

1.744

− 10.42

1.748

− 10.42

Heated

45

6.85

1.924

− 10.27

1.927

− 10.27

Heated

46

8.20

2.104

− 10.10

2.116

− 10.11

Heated

47

8.01

2.081

− 10.15

2.078

− 10.15

Heated

48

8.67

2.160

− 10.05

2.173

− 10.07

Heated

49

10.76

2.376

− 9.83

2.407

− 9.87

Not-heated

50

8.38

2.126

− 10.08

2.138

− 10.09

Heated

51

6.29

1.839

− 10.34

1.842

− 10.34

Heated

52

7.88

2.064

− 10.14

2.073

− 10.15

Heated

53

7.82

2.057

− 10.16

2.059

− 10.16

Heated

54

7.86

2.061

− 10.14

2.071

− 10.15

Heated

55

5.60

1.723

− 10.46

1.715

− 10.45

Heated

56

8.38

2.126

− 10.07

2.143

− 10.09

Heated

57

8.12

2.094

− 10.09

2.115

− 10.11

Heated

58

8.59

2.151

− 10.05

2.168

− 10.07

Heated

59

9.42

2.243

− 9.95

2.269

− 9.98

Not-heated

60

6.45

1.864

− 10.35

1.852

− 10.34

Heated

61

10.70

2.370

− 9.87

2.384

− 9.89

Not-heated

62

6.56

1.881

− 10.34

1.867

− 10.32

Heated

63

10.02

2.305

− 9.91

2.326

− 9.94

Not-heated

64

8.04

2.084

− 10.14

2.085

− 10.14

Heated

65

10.95

2.393

− 9.90

2.384

− 9.89

Not-heated

66

6.72

1.904

− 10.35

1.876

− 10.32

Heated

67

10.64

2.365

− 9.86

2.389

− 9.88

Not-heated

68

8.68

2.161

− 10.04

2.179

− 10.06

Heated

69

8.56

2.147

− 10.05

2.166

− 10.07

Heated

70

12.16

2.498

− 9.79

2.499

− 9.79

Not-heated

71

10.91

2.390

− 9.92

2.373

− 9.90

Not-heated

72

11.53

2.445

− 9.82

2.456

− 9.83

Not-heated

73

9.09

2.207

− 10.06

2.196

− 10.05

Indet.

74

5.45

1.696

− 10.47

1.695

− 10.47

Heated

75

12.11

2.494

− 9.80

2.491

− 9.80

Not-heated

76

12.29

2.509

− 9.75

2.523

− 9.77

Not-heated

77

14.35

2.664

− 9.65

2.663

− 9.65

Not-heated

78

6.16

1.818

− 10.40

1.801

− 10.38

Heated

79

6.80

1.917

− 10.20

1.957

− 10.25

Heated

80

11.89

2.476

− 9.86

2.454

− 9.83

Not-heated

81

9.13

2.212

− 10.05

2.203

− 10.04

Indet.

82

8.18

2.102

− 10.19

2.070

− 10.15

Heated

83

7.03

1.950

− 10.26

1.947

− 10.26

Heated

84

9.25

2.225

− 10.03

2.221

− 10.03

Indet.

85

13.85

2.628

− 9.73

2.603

− 9.70

Not-heated

86

10.63

2.364

− 9.86

2.386

− 9.89

Not-heated

87

9.30

2.230

− 10.01

2.234

− 10.01

Not-heated

88

4.67

1.542

− 10.64

1.521

− 10.62

Heated

89

9.89

2.292

− 9.95

2.297

− 9.96

Not-heated

90

5.40

1.687

− 10.51

1.670

− 10.49

Heated

91

13.39

2.595

− 9.75

2.573

− 9.73

Not-heated

92

6.07

1.804

− 10.25

1.866

− 10.32

Heated

93

9.23

2.222

− 9.99

2.237

− 10.01

Not-heated

94

6.28

1.837

− 10.33

1.846

− 10.34

Heated

95

8.00

2.079

− 10.10

2.101

− 10.13

Heated

96

9.69

2.271

− 9.93

2.296

− 9.96

Not-heated

97

4.44

1.492

− 10.68

1.472

− 10.66

Heated

98

7.23

1.979

− 10.21

1.988

− 10.22

Heated

99

10.71

2.371

− 9.87

2.384

− 9.89

Not-heated

100

5.67

1.735

− 10.44

1.732

− 10.44

Heated

Table 6

Relative prevalence of heat-treated silcrete in Level III at Burrill Lake as determined by surface roughness measurements. Values under ‘n’ are numbers summarised from Table 5 and values under ‘%’ are percentages calculated to the base of all determinable pieces (excluding the pieces plotting in the indeterminate zone of Fig. 5)

 

n

% det.

Analysed pieces

100

 

Indet

4

 

Heat-treated

60

62.5

Not-heated

36

37.5

Discussion

The Quality of Our Data

Studies on silcrete heat treatment similar to the one published here are so far only known from the Southern African MSA and LSA (see for example: Schmidt and Mackay 2016). This is the first time that this methodology for quantifying the relative prevalence of heat treatment in assemblages has been applied to the Australian landmass. Here we have verified the results from our visual identification by means of an independent objective measurement of the surface roughness to demonstrate that the methods elaborated in Africa are equally applicable to Australian silcrete. For example, comparison between both approaches for level III at Burrill Lake resulted in impressively similar numbers: visual classification identified 61.9% as heat-treated and 38.1% as untreated, whereas surface roughness measurements identified 62.5% as heat-treated and 37.5% as untreated. Even the percentage of indeterminable artefacts was the same in both approaches. We observe that our visual identification accurately reflects the different fracture patterns on the Burrill Lake silcrete. A supplementary measure of the quality of our data is the finding that the two oldest, but contemporaneous, layers at Burrill Lake yielded very similar percentages of heated pieces. The observed relative prevalence of silcrete heat treatment seems indeed to be time-dependent (Fig. 6).
Fig. 6

Histograms comparing the relative prevalence of heat treatment in the silcrete components of all analysed assemblages. a Relative prevalence of heat treatment in different layers at Burrill Lake. b Comparison of the relative prevalence of heat treatment as a function of time. Bur.L. = Burrill Lake; K II = Shaw’s Creek. Assemblages are arranged as a function of time, from the oldest at the bottom to the youngest at the top. The assemblage from Shaw’s Creek Phase VI is not dated and is therefore only shown as grey bar with question marks. Note the increasing relative prevalence through time

Visual classifications of pre- and post-heating fracture patterns will need to be tailored to each region, as we have done in this paper for some Australian silcretes, but there is no reason to expect that Australian silcretes would behave fundamentally differently than the African silcrete previously analysed by this method (see for example: Schmidt and Mackay 2016; Schmidt et al. 2015). Materials from both continents have been described as being similar in terms of mineralogy, chemistry and texture (Summerfield 1983c) and there are numerous earlier studies on Australian silcrete that inferred heat treatment on the basis of the transformation of fracture patterns (see for example: Cochrane et al. 2012). Additionally, when experimentally heat-treated, reference samples were analysed in the same way as artefacts, we observed similar absolute roughness values and similar magnitude of change in both groups. These data strongly suggest that the roughness differences measured on silcrete from the study area are good proxies for silcrete artefacts knapped after heat treatment. It can therefore be expected that our results on the relative prevalence of heat treatment in the analysed silcrete assemblages is reliable and can be used to interpret the ancient heating behaviours in the Sydney area and beyond.

A Regional Pattern of Silcrete Heat Treatment in the Sydney Area

We propose that evidence presented here is consistent with a long-term directional change towards greater relative abundance of heat-treated artefacts in the Sydney Basin and Hunter Valley. While we acknowledge that we have sampled only three sites, we note the consistency of the temporal pattern as represented in these three different places. More data points will no doubt be added in future research, testing our model, but here we focus on a discussion of the possible reasons for such directional change.

What causes the observed change through time? We suggest there is more than one explanation that may be considered, and that several processes worked probably in conjunction to create selective contexts in which heat treatment was emphasised. One factor likely to have been in operation is the increased emphasis on abundance technological strategies during the Holocene. Those abundance strategies focused on the production of large numbers of items that individually have low utility, with small potential for use-life or resharpening (Hiscock 2006). Such strategies became increasingly common in Holocene Australia, supplementing pre-existing extension strategies which focused on comparatively few specimens that are individually reworked to exaggerate potential use-life, thereby extending utility (Hiscock and Attenbrow 2003). On Australia’s eastern seaboard abundance, strategies had long been in place in the form of small unretouched flakes from the very start of the sequence and subsequently in the form of microliths from the beginning of the Holocene (Hiscock and Attenbrow 2004, 1998). However, the emphasis on abundance strategies increased dramatically to approximately 3.5–2.5 ka, when a proliferation of microlith production saw them produced in extraordinary quantities (Hiscock 2008). The Late Holocene switch from an emphasis on fewer extensively maintained tools to more abundant minimally resharpened tools provides a context for the increasing employment of heat treatment. While heat treatment enhances fracture propagation and control by the knapper (Domanski et al. 1994; Schindler et al. 1982), it also typically reduces edge strength (see for example: Léa et al. 2012), and hence heat treatment may be seen to assist abundance strategies more than extension ones, since for an abundance strategy, reductions in edge strength may be adequately compensated by the increased number of tools available. The value of heat treatment for an abundance strategy such as microlith production is the ability of knappers to exercise greater control over the production of small and regularly shaped blanks as well as enhancing fine removal of small backing retouch flakes without breaking the object. Therefore, one model to potentially explain our data is that the increase in heat-treated products in the archaeological record is a reflection of more frequent or intense use of heat treatment procedures to facilitate the manufacture of microliths and related materials. Refitting of microliths and manufacturing debris in the Sydney and Hunter regions has commonly established a functional association, in which heat treatment occurred prior to blank production (Hiscock 1993a; Hiscock 2006). We hypothesise that the greater amount of heat-treated debris in Mid- to Late Holocene assemblages may be a product of (i) altered core reduction strategies, (ii) increased core reduction intensity and (iii) production of large numbers of backing flakes. Any combination of these factors would increase the relative abundance of heat treat debris as microlith production ramped-up 3.5–2.5 ka.

However, the gradual and prolonged increases in the efforts invested in searching for higher quality knappable rocks, as well as transporting and thermally modifying suitable materials of higher quality, are not well explained by a single event or short-term process such as the backed artefact proliferation. The evolutionary trajectory we document requires a persistent process or a series of selective contexts with similar effect on the technological system. This means that while altered technological strategies provide a plausible explanation for the drastic increase of heat treatment in the latter part of the Holocene, the earlier onset of this process still remains explained by other mechanisms. We begin by noting the archaeological evidence for long-term trends in raw material selection. A shift towards greater amounts of heat-treated specimens in assemblages is one part of a long-term trend towards the use of ‘more expensive’ and/or higher quality materials for formal tool production. Altered raw material procurement has been observed on the eastern seaboard, beginning earlier than the microlith proliferation, at least in some locations (Hiscock 2008). One of the clearest examples comes from Capertee 3, a rock shelter located between Burrill Lake and Shaws Creek, which displays increases in the procurement of high-quality rocks for non-microlithic retouched flakes beginning up to 8 ka, before the increased microlith production began (Hiscock and Attenbrow 2004). That site has virtually no silcrete and displays changes in material selection over time as non-local high-quality chert was increasingly brought to the site. That trend is apparent in the early Holocene for non-microlith components of the technology and extends into microlith production later in time. Hence, we have evidence of a long history to heat treatment in the Sydney and Hunter regions where there were trends to selection of higher grade over time, providing a context in which better materials were not only preferentially selected from those available in the landscape but also created through thermal alteration of rocks. What selective context would explain this long-term increase in investment in transporting and/or modifying knappable materials?

One context that helps explicate these long-term trends is overexploitation of lithic raw material sources (Dibble 1991). With slow erosion rates across much of this Australian region, the resupply of new nodules of knappable rock was often slow in most locations, limiting the lithic reserves that were available for inspection and selection (see: Hughes 1977 for sedimentation rates). Over time, as higher quality nodules were preferentially selected and knapped, it would have become harder to find a comparable rock in the landscape. In this context, it was advantageous for knappers to become increasingly prepared to invest in transporting good-quality rocks encountered in the landscape and to alter the properties of silcretes that are available. This process would be directional as the preferential demand for better quality would progressively reduce the availability of those materials, encouraging progressive increases in heat treatment to compensate, creating a feedback system driving intensification of the trend. Any increase in Aboriginal population size over time, suggested by a number of researchers (Williams 2013), would have increased demands for lithic tools, accelerating depletion of the lithic resources.

Alteration of lithic foraging patterns and increasing investment in thermal treatment in response to depletion of high-ranked materials will reflect the distribution and character of lithic materials in each catchment within the study areas, but there may also have been cultural factors creating material shortages. For example, there has been much discussion by Australian archaeologists about the emergence of the territorial boundaries that were observed in the historic period (Lourandos 1997; David and Lourandos 1998; Tacon 1993). If the emergence of historical identities constructed obstacles to movement and direct procurement of material, then this might increase rarity and/or cost of higher grade materials. While most existing models propose a late emergence of historical boundaries, the nature and antiquity of such boundaries are poorly defined. Perhaps more significant than the formation emergence of landscape boundaries would have been fluctuations in those boundaries, creating uncertainty in material supply. Such culturally defined barriers to procuring material may have contributed to the depletion feedback process. In any case, a strong pattern of depletion of higher grade lithics would yield predictions such as the increased use of lower rank raw materials even as heat treatment increased. Increases in quartz use at the end of the Holocene is often observed in sequences within the Sydney basin and may be one reflection of the increased uptake of lower ranked materials (Attenbrow 2010).

We have described the presence of multiple selective pressures acting on the material acquisition decisions and tool production strategies of Australian foragers during the late Pleistocene and Holocene, and we hypothesise that these pressures combined to intensify heat treatment rates and create long-term trends in the frequency of heat-treated specimens. We suggest the effects of long-term selection processes encouraging the use of higher grade materials, such as gradual depletion of lithic resources, were significantly compounded by the onset of the backed artefact proliferation event and perhaps the construction of new and/or changeable political barriers to material flow. Each of these processes would have created situations in which it was advantageous to apply heat treatment more frequently over time.

Conclusion

In this paper, we have used methods already employed in Africa but novel in Australia to infer long-term directional changes in the relative frequency of silcrete artefacts that were heat-treated. Our methods involved independent but also cross-verifying tests of the presence or absence of heat treatment, employing visual classifications and surface roughness measures. Application of these methods to archaeological sequences in the landscapes around Sydney revealed a coherent series of increases over time, so that Late Holocene assemblages displayed higher rates of heat treatment than terminal Pleistocene or Early Holocene ones. We hypothesise that the directional trend towards greater frequencies of heat-treated artefacts on Australia’s eastern seaboard is explicable in terms of the context of technological shifts towards microlith production superimposed onto an even longer term process of lithic resource depletion, perhaps compounded with the development of political barriers to the redistribution of knappable stone. We view the long-term evolution in the rate of heat treatment in Eastern Australia as a technological response to dynamic industrial and social contexts that developed in the last 25 millennia.

Notes

Acknowledgements

We thank the Australian Museum for permission to access their collections. We are thankful to A. Dejanovic for all her help and assistance during data analysis in the Australian Museum and to A. Mackay for providing one of the silcrete reference samples.

Funding Information

Financial support for this study was provided by the Deutsche Forschungsgemeinschaft (DFG) (grant number SCHM 3275/2-1), and by the Tom Austen Brown Endowment, University of Sydney.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Early Prehistory and Quaternary EcologyEberhard Karls University of TübingenTübingenGermany
  2. 2.Department of Geosciences, Applied MineralogyEberhard Karls University of TübingenTübingenGermany
  3. 3.Department of ArchaeologyUniversity of SydneySydneyAustralia
  4. 4.Australian Museum Research InstituteAustralian MuseumSydney SouthAustralia

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