Identifying Changepoints in Population Size through Radiocarbon Determinations
Lead Research Organisation:
University of Leeds
Department Name: Statistics
Abstract
An 8-month project to develop, and test, novel statistical tools that will enable environmental, and archaeological, scientists to accurately identify changes in the past size of populations from samples dated via radiocarbon. Studying changes in patterns of occupation is key to understanding our responses to past environmental and societal changes, and to forecasting our resilience to potential future climate change. It is essential to provide a statistically-rigorous alternative to replace some of the incoherent approaches currently used.
A frequently-used archaeological approach to estimating changes in the size of a population is to consider how the number of samples relating to that population (e.g., human/animal bones, or other evidence of occupation) varies over time. Time periods for which there are large numbers of samples are expected to correspond to a larger population being present; periods when fewer samples are found suggest a smaller population size. Such an approach can be applied to individual sites, or by collating samples across multiple sites.
The reliability of such a "dates-as-data" approach is highly dependent upon our ability to estimate the calendar ages of the discoveries. When studying the last 55,000 years, the most common way to obtain the necessary dates is via radiocarbon. Unfortunately, the need to calibrate all radiocarbon determinations introduces considerable, and complex, uncertainties into the calendar ages of each of the samples. This uncertainty should be incorporated into later inference such as when using the density of the dates as a proxy for population size. However, it is well recognised that the techniques currently used by the radiocarbon community fail to do so fully. This limits the reliability of the inference they provide. New statistically-rigorous tools are required. This project will provide them.
We will develop an integrated Bayesian framework that is able to jointly calibrate the radiocarbon determinations belonging to a set of samples, and to identify if there are statistically-significant changes in the rate at which the samples arise. We will demonstrate our approach on a range of geoscientifically-interesting questions including the expansion of humans into the Yukon and Alaska in the late Pleistocene and early Holocene. We will aim to investigate both the timings of such migrations in comparison with the climatic changes known to have occurred during this period, and to study the potential interactions between humans and other megafauna in the region.
In addition to developing and publishing the statistical framework, we will also work to ensure our work is of maximum benefit to the target radiocarbon user community. We will disseminate our research through provision of usable and easily-accessible software; and a user-guide providing illustrative cases studies and worked examples, that is appropriately written for the target archaeological and environmental science community.
A frequently-used archaeological approach to estimating changes in the size of a population is to consider how the number of samples relating to that population (e.g., human/animal bones, or other evidence of occupation) varies over time. Time periods for which there are large numbers of samples are expected to correspond to a larger population being present; periods when fewer samples are found suggest a smaller population size. Such an approach can be applied to individual sites, or by collating samples across multiple sites.
The reliability of such a "dates-as-data" approach is highly dependent upon our ability to estimate the calendar ages of the discoveries. When studying the last 55,000 years, the most common way to obtain the necessary dates is via radiocarbon. Unfortunately, the need to calibrate all radiocarbon determinations introduces considerable, and complex, uncertainties into the calendar ages of each of the samples. This uncertainty should be incorporated into later inference such as when using the density of the dates as a proxy for population size. However, it is well recognised that the techniques currently used by the radiocarbon community fail to do so fully. This limits the reliability of the inference they provide. New statistically-rigorous tools are required. This project will provide them.
We will develop an integrated Bayesian framework that is able to jointly calibrate the radiocarbon determinations belonging to a set of samples, and to identify if there are statistically-significant changes in the rate at which the samples arise. We will demonstrate our approach on a range of geoscientifically-interesting questions including the expansion of humans into the Yukon and Alaska in the late Pleistocene and early Holocene. We will aim to investigate both the timings of such migrations in comparison with the climatic changes known to have occurred during this period, and to study the potential interactions between humans and other megafauna in the region.
In addition to developing and publishing the statistical framework, we will also work to ensure our work is of maximum benefit to the target radiocarbon user community. We will disseminate our research through provision of usable and easily-accessible software; and a user-guide providing illustrative cases studies and worked examples, that is appropriately written for the target archaeological and environmental science community.
Publications
Talamo S
(2023)
Atmospheric radiocarbon levels were highly variable during the last deglaciation
in Communications Earth & Environment
| Description | A commonly-used paradigm to estimate changes in the frequency of past events or the size of populations is to consider the occurrence rate of archaeological/environmental samples found at a site over time. The reliability of such a "dates-as-data" approach is highly dependent upon how the occurrence rates are estimated from the underlying samples, particularly when calendar age information for the samples is obtained from radiocarbon (14C). The most frequently-used "14C-dates-as-data" approach of creating Summed Probability Distributions (SPDs) is not statistically valid or coherent and can provide highly misleading inference. Thought this research, we have provided an alternative method with a rigorous statistical underpinning that also provides valuable additional information on potential changepoints in the rate of events. Our approach ensures more reliable "14C-dates-as-data" analyses, allowing us to better assess and identify potential signals present. We model the occurrence of events, each assumed to leave a radiocarbon sample in the archaeological/environmental record, as an inhomogeneous Poisson process. The varying rate of samples over time is then estimated within a fully-Bayesian framework using reversible-jump Markov Chain Monte Carlo (RJ-MCMC). Given a set of radiocarbon samples, we reconstruct how their occurrence rate varies over calendar time and identify if that rate contains statistically-significant changes, i.e., specific times at which the rate of events abruptly changes. We illustrate our method with both a simulation study and a practical example concerning late-Pleistocene megafaunal population changes in Alaska and Yukon. We have created a new R library and an online user guide (with worked examples and vignettes) available open access at https://tjheaton.github.io/carbondate/ that allows others easily use our methods. The work has been presented at several conferences to the relevant user communities including to environmental scientists at EGU in Vienna (https://www.egu24.eu) and archaeologists at EAA in Rome (https://www.e-a-a.org/EAA2024). It has also presented at ETH, Helsinki, and Bergen. We have a pre-print under review available at ArXiV: https://arxiv.org/abs/2501.15980 |
| Exploitation Route | We have also provided an R package and online user guide to allow all the radiocarbon community to implement our methods broadly: https://tjheaton.github.io/carbondate/ This project has also led on to further research ideas with funding from the Royal Society and British Academy. |
| Sectors | Environment Culture Heritage Museums and Collections |
| Title | R library |
| Description | Generated an R library and website: https://tjheaton.github.io/carbondate/ |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | None to date - only just released |
| URL | https://tjheaton.github.io/carbondate/ |
| Title | carbondate library |
| Description | R library to implement methods |
| Type Of Material | Data analysis technique |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | None to date - only just published |
| URL | https://tjheaton.github.io/carbondate/ |
| Description | Bard - Solar Storm |
| Organisation | European Centre for Research and Teaching of Environmental Geosciences (CEREGE) |
| Country | France |
| Sector | Academic/University |
| PI Contribution | I fitted a floating tree ring 14C sequence from S France to existing data, identifying the largest ever solar storm 14,300 cal yr BP ago as identified in individual curve realisations |
| Collaborator Contribution | E Bard and colleagues conceptualised the work, provided the trees, and the 14C and dendrochronology measurements |
| Impact | https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0206#d1e440 This paper was highlighted as one of the top 10 papers (in terms of Almetrics) published by the Royal Society last year |
| Start Year | 2023 |
| Description | Press Release and Media Coverage - Solar Storm |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Press Coverage for Solar Storm Paper - see altmetric for: https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0206#d1e440 |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.newscientist.com/article/2396456-largest-known-solar-storm-struck-earth-14300-years-ago/ |
