Bayesian chronological modelling of the Early Iron Age in Southern Jutland, Denmark

The dissertation investigates temporal processes of change in Early Iron Age material culture from Southern Jutland, Denmark (c.500-200 BC). The chronological framework of the period is mainly based on typo-chronological analyses of metalwork, and to a lesser degree of pottery, from large urnfield cemeteries. The chronological framework is unconstrained by scientific dating, which makes it difficult to correlate metalwork and pottery that appear disproportionate in funerary and settlement contexts. The dissertation continues a long tradition of chronological research using artefact assemblages from the urnfields, but it also presents the first large-scale 14C dataset in support of an absolute chronological framework. The majority of datable material from the urnfields is cremated bone and because 14C dating cremated bone is a relatively new method, it is necessary to investigate methodological aspects relating to laboratory techniques. A laboratory intercomparison demonstrates that differences in pretreatment do not affect the radiocarbon results and that results are reproducible between laboratories (Rose et al. 2019). Another important aspect when dating cremated bone is that it will be affected by wood-age offsets through carbon substitution, effectively causing cause calendar date offsets between the real cremation events and the radiocarbon dates. The dissertation explores statistical modelling of offsets through additional case studies (Rose et al. 2018; Kristiansen et al. forthcoming), before proposing a new statistical model aimed at wood-age offsets in cremated bone (Rose 2020). The new statistical model is based on the empirical distribution of wood-age offset in an experimental dataset and on archaeological combinations of cremated bone and archaeobotanical remains. The statistical model is applied to all dates on cremated bone from 95 burials from three urnfields, Aarupgaard, Aarre and Søhale, in order to take account of their inherent wood-age offsets. The corrected dates provide indirect dates on the associated artefacts of metalwork and pottery and together with prior information on artefact typology and site formation processes (e.g. how the urnfields developed spatially over time) this is combined in a Bayesian framework aimed at modelling temporal processes of change in the material culture. 24 artefact types are modelled and out of these, nine metalwork and five pottery currencies have sufficient dates to be modelled independently. The models support an existing typological sequence of metalwork, but it also suggests that the ceramic sequence needs to be revised. The models suggest discrepancies in existing ceramic sequences (Jensen 2005), and in contrast to earlier works (Becker 1961; Jensen 2005), it is proposed that pins with circular head is treated as a single type regardless of head size. It is also proposed to divide the different types of belt equipment into an early group and a late group to minimize misclassification. The currency models provide a necessary correlation of metalwork and pottery, but they also offer independent evidence of the temporal processes of change in material culture by demonstrating periods with particularly rapid and slower change of the Early Iron Age material culture. The currency models have inhomogeneous temporal structures, with most metalwork being in use for shorter periods than pottery, but how quickly or slowly a type was introduced, and later abandoned, is very individual. Overall metalwork is found to be chronological sensitive, whereas pottery appears to be more conservative in relation to change. The dissertation evaluates the chronological framework based on the new 14C evidence and the transitional from Bronze to Iron Age in Southern Jutland is estimated as a period starting in the early 7th century BC. The Bronze-Iron Age transformation is traditionally considered to occur c.500 BC and to coincide with the introduction of the urnfield phenomenon, but the urnfields are now estimated to start already in the early 6th century BC. The urnfields are abandoned in the last half of the 3rd century BC, which coincides with the traditional transformation from Early to Late Pre-Roman Iron Age c.250-200 BC

RADIOCARBON DATING CREMATED BONE:A CASE STUDY COMPARING LABORATORY METHODS

Radiocarbon (C-14) results on cremated bone are frequently published in high-ranking journals, but C-14 laboratories employ different pretreatment methods as they have divergent perceptions of what sources of contaminants might be present. We found pretreatment protocols to vary significantly between three laboratories (Brussels [RICH], Kid [KIA], and Groningen [CIO]), which all have a long history of dating cremated bone. We present a case study of 6 sets of replicate dates, to compare laboratory pretreatment protocols, and a further 16 sets of inter-laboratory replicate measurements, which compare specific steps of the conversion and measuring process. The C-14 results showed dates to be reproducible between the laboratories and consistent with the expected archaeological chronology. We found that differences in pretreatment, conversion to CO2 and accelerator mass spectrometry (AMS) measurement to have no measurable influence on the majority of obtained results, suggesting that any possible diagenesis was probably restricted to the most soluble</p

Aarupgaard tuegravplads gennem 75 år: Foreløbige betragtninger

Aarupgaard urnfield is situated in SouthernJutland, between Gram and Ribe, and visitorscan still today catch a glimpse of the largestbarrows in the tall grass. The urnfield wasdiscovered in the late 19th century and althoughminor excavations were carried outover the years, it was excavated almost inits entirety by Erik Jørgensen from MuseumSønderjylland in 1970 – 1972. The excavationsrevealed c. 1300 well-preserved urnfieldgraves, makingAarupgaard the largestknown urnfield in Denmark.Aarupgaard urnfield has the potential tobecome a cornerstone in our understandingof Early Iron Age societies, but although manyyears have passed since its excavation and thematerial has been included in more projectsover the years, the site has never been fullypublished. A working group was therefore establishedin 2018, with the aim of preparingAarupgaard urnfield for publication. Digitalregistration of the archival material alone is aconsiderable task, but this is hopefully not thelast we hear of Aarupgaard urnfield

Bayesian chronological modelling in Danish archaeology

Bayesian chronological modelling in Danish archaeologyIn Danish archaeology, radiocarbon dating has become an integrated part of the archaeological toolbox. A certain scepticism towards the accuracy of the method means, however, that it often remains a supplement to archaeological interpretation and other informal dating methods.Bayesian modelling counters this scepticism by combining radiocarbon dating with archaeological observations and other dating methods such as stratigraphy, dendrochronology and numismatic dating. Based on data from sources such as these, a Bayesian model calculates the statistical probability distribution of individual radiocarbon dates. Because the modelled dates consider all available information relating to the samples and their context, they can produce more accurate, robust dates and chronologies than those based on simple calibrated dates. Moreover, through Bayesian modelling, it is possible to estimate the dates of events that cannot be dated directly, such as the beginning or end of a settlement phase.The benefits of implementing Bayesian modelling in Danish archaeology are considerable. However, given that it can seem confusing and difficult to comprehend, in the following we will introduce the method by presenting and discussing some examples of Bayesian modelling.Radiocarbon dates are probabilistic, which means that each radiocarbon measurement holds considerable uncertainty. Each radiocarbon date is expressed as a bell-shaped normal distribution around a median (fig. 1). The date is reported as a radiocarbon age and a standard deviation (e.g. 1690 BP ± 30 years). The uncertainty of the radiocarbon measurement is often increased through calibration to calendar years by matching with the wiggles and plateaus of the calibration curve. Since calibrated radiocarbon dates are distributed around the radiocarbon sample’s actual age, a visual assessment of the calibration plot will often lead to misinterpretation of the date of specific events or the beginning, end, or duration of phases. Bayesian statistics is a way of countering these uncertainties and misinterpretations. In the following, we use the calibration program OxCal. The first example is a fictional case where ten simulated radiocarbon dates, corresponding to known years at 10-year intervals between AD 970 and 1060, are calibrated (fig. 2). From a visual assessment of the calibrated dates, it seems they are contemporaneous since the probability distribution of each individual date is up to 200 years. The wide probability distribution blurs the fact that the events are each ten years apart. If we add the prior information that the events form a sequence in which sample A is older than sample B etc., the modelled dates then display narrower probability distributions (fig. 3). These are called posterior probability distributions. A so-called Boundary is added to the model to limit the sequence and mark the first and last non-dated event, since it is unlikely that a sample representing the first and last event in a sequence has been taken.Stratigraphic information is termed an informative prior, while an uninformative prior represents a situation where the only information about the samples is that they belong to the same phase. Uninformative priors are illustrated by five samples from the postholes of an Iron Age house. The house had been in use for 30 years. The simulated dates are then placed within a 30-year period (fig. 4). Again, the unmodelled dates blur the actual duration of the use-phase of the house. The prior information that the samples are interpreted as being contemporaneous is now added to the model using the Phase command. The model then estimates when the use of the house began and when it ended. In OxCal, the Agreement Index, A, is an indicator of the match between the data and the model. It is based on the overlap of the calibrated probability distribution and the posterior distributions. An Agreement Index below 60% is an indication of a problematic sample. An Agreement Index is also calculated for the whole model (Amodel).In a more complex example, stratigraphical information regarding the Iron Age house is added. Samples from a stratigraphically younger house and a younger pit are added to the model as two phases in a sequence (fig. 5). The three samples from the pit are regarded as being contemporaneous, and the ‘combine’ command is used.In the simulated example, five samples are enough to create a robust model for a house’s use-phase. But the number of samples needed also depends on where on the calibration curve the dates end up, and a small number of samples may be compensated for by strong priors. The following example is not simulated but from the excavation of an Iron Age house. Three samples were taken from a roof-bearing post. The samples were taken from growth rings spaced, respectively, 10 and 12 years apart. The charred remains of hazel wattle were found between the stones in the cobbled floor (fig. 6). The hazel was interpreted as the remains of the wattle walls of the excavated house. Two ditches surrounding the house were sampled, one of which was stratigraphically older than the other. The samples and the prior information were combined in a Bayesian model. The house’s date was narrowed from 158-8 BC to 91-6 BC (fig. 7).The final example is from the excavation of a medieval house in a town (fig. 10). Five samples from the floors in the house’s basement were added in a Bayesian model (fig. 9). The floors superseded each other. Moreover, two dendrochronological samples from latrine barrels, older than the house, and samples from the barrels’ content were added to the model (fig. 10). Based on the archaeological interpretation, the use of the house was dated to between AD 1250 and 1450. However, the model showed that it was more likely to have been in use between AD 1413 and 1487 (fig. 11). This new date suggests that the potsherds found in the floor layers were redeposited. These examples demonstrate the considerable potential of Bayesian modelling. However, they also show that it is essential to exercise great care in constructing the model and providing a thorough account of the archaeological priors. It may be necessary to create several models to test the priors’ robustness or to test different priors. Bayesian modelling presents a systematic and formalised way to test various archaeological interpretations.Bayesian chronological modelling in Danish archaeologyIn Danish archaeology, radiocarbon dating has become an integrated part of the archaeological toolbox. A certain scepticism towards the accuracy of the method means, however, that it often remains a supplement to archaeological interpretation and other informal dating methods.Bayesian modelling counters this scepticism by combining radiocarbon dating with archaeological observations and other dating methods such as stratigraphy, dendrochronology and numismatic dating. Based on data from sources such as these, a Bayesian model calculates the statistical probability distribution of individual radiocarbon dates. Because the modelled dates consider all available information relating to the samples and their context, they can produce more accurate, robust dates and chronologies than those based on simple calibrated dates. Moreover, through Bayesian modelling, it is possible to estimate the dates of events that cannot be dated directly, such as the beginning or end of a settlement phase.The benefits of implementing Bayesian modelling in Danish archaeology are considerable. However, given that it can seem confusing and difficult to comprehend, in the following we will introduce the method by presenting and discussing some examples of Bayesian modelling.Radiocarbon dates are probabilistic, which means that each radiocarbon measurement holds considerable uncertainty. Each radiocarbon date is expressed as a bell-shaped normal distribution around a median (fig. 1). The date is reported as a radiocarbon age and a standard deviation (e.g. 1690 BP ± 30 years). The uncertainty of the radiocarbon measurement is often increased through calibration to calendar years by matching with the wiggles and plateaus of the calibration curve. Since calibrated radiocarbon dates are distributed around the radiocarbon sample’s actual age, a visual assessment of the calibration plot will often lead to misinterpretation of the date of specific events or the beginning, end, or duration of phases. Bayesian statistics is a way of countering these uncertainties and misinterpretations. In the following, we use the calibration program OxCal. The first example is a fictional case where ten simulated radiocarbon dates, corresponding to known years at 10-year intervals between AD 970 and 1060, are calibrated (fig. 2). From a visual assessment of the calibrated dates, it seems they are contemporaneous since the probability distribution of each individual date is up to 200 years. The wide probability distribution blurs the fact that the events are each ten years apart. If we add the prior information that the events form a sequence in which sample A is older than sample B etc., the modelled dates then display narrower probability distributions (fig. 3). These are called posterior probability distributions. A so-called Boundary is added to the model to limit the sequence and mark the first and last non-dated event, since it is unlikely that a sample representing the first and last event in a sequence has been taken.Stratigraphic information is termed an informative prior, while an uninformative prior represents a situation where the only information about the samples is that they belong to the same phase. Uninformative priors are illustrated by five samples from the postholes of an Iron Age house. The house had been in use for 30 years. The simulated dates are then placed within a 30-year period (fig. 4). Again, the unmodelled dates blur the actual duration of the use-phase of the house. The prior information that the samples are interpreted as being contemporaneous is now added to the model using the Phase command. The model then estimates when the use of the house began and when it ended. In OxCal, the Agreement Index, A, is an indicator of the match between the data and the model. It is based on the overlap of the calibrated probability distribution and the posterior distributions. An Agreement Index below 60% is an indication of a problematic sample. An Agreement Index is also calculated for the whole model (Amodel).In a more complex example, stratigraphical information regarding the Iron Age house is added. Samples from a stratigraphically younger house and a younger pit are added to the model as two phases in a sequence (fig. 5). The three samples from the pit are regarded as being contemporaneous, and the ‘combine’ command is used.In the simulated example, five samples are enough to create a robust model for a house’s use-phase. But the number of samples needed also depends on where on the calibration curve the dates end up, and a small number of samples may be compensated for by strong priors. The following example is not simulated but from the excavation of an Iron Age house. Three samples were taken from a roof-bearing post. The samples were taken from growth rings spaced, respectively, 10 and 12 years apart. The charred remains of hazel wattle were found between the stones in the cobbled floor (fig. 6). The hazel was interpreted as the remains of the wattle walls of the excavated house. Two ditches surrounding the house were sampled, one of which was stratigraphically older than the other. The samples and the prior information were combined in a Bayesian model. The house’s date was narrowed from 158-8 BC to 91-6 BC (fig. 7).The final example is from the excavation of a medieval house in a town (fig. 10). Five samples from the floors in the house’s basement were added in a Bayesian model (fig. 9). The floors superseded each other. Moreover, two dendrochronological samples from latrine barrels, older than the house, and samples from the barrels’ content were added to the model (fig. 10). Based on the archaeological interpretation, the use of the house was dated to between AD 1250 and 1450. However, the model showed that it was more likely to have been in use between AD 1413 and 1487 (fig. 11). This new date suggests that the potsherds found in the floor layers were redeposited. These examples demonstrate the considerable potential of Bayesian modelling. However, they also show that it is essential to exercise great care in constructing the model and providing a thorough account of the archaeological priors. It may be necessary to create several models to test the priors’ robustness or to test different priors. Bayesian modelling presents a systematic and formalised way to test various archaeological interpretations

Temporal dynamics of Linearbandkeramik houses and settlements, and their implications for detecting the environmental impact of early farming

Long-held ideas concerning early Neolithic Linearbandkeramik (LBK) settlements in central Europe have been thoroughly challenged in recent years, for example, regarding their internal organisation or the use-life of individual houses. These topics have now also been addressed with the help of large radiocarbon (14C) datasets. In the light of this discussion, we present findings of our ongoing research at Vráble in south-western Slovakia. Intensive prospection by fieldwalking, geophysics and sedimentology, complemented by targeted excavations and archaeobotanical investigations, aims to unravel social and temporal relationships between three adjacent LBK settlements. A total of 23 of the c.300 houses revealed by geophysical prospection have been dated. Bayesian chronological modelling of this dataset, comprising 109 14C ages from 104 samples, indicates that the three LBK settlements at Vráble coexisted, and that overall the LBK settlement lasted for c. 200–300 years. Our results imply a ‘short’ use-life for individual houses (median c.20–30 y), suggesting that relatively few houses were inhabited simultaneously. Our data suggest that the overall LBK population at Vráble might have increased over the course of occupation, but probably never exceeded 200–300 individuals, based on the number of houses that could have been occupied contemporaneously. We compare the Vráble evidence with Bayesian chronologies for other LBK sites, and discuss the implications of these findings for models of population agglomeration and recognising the environmental impact of early farming communities