- Email: [email protected]
Introduction to the Frison Institute Symposium on radiocarbon dating applications
Journal of Archaeological Science 52 (2014) 546e548
Contents lists available at ScienceDirect
Journal of Archaeological Science journal homepage: http://www.elsevier.com/locate/jas
Introduction to the Frison Institute Symposium on radiocarbon dating applications Robert L. Kelly a, *, Nicolas Naudinot b, 1 a b
Dept. of Anthropology, University of Wyoming, Laramie WY 82071, USA Universit e de Nice Sophia Antipolis, Campus Saint-Jean-d'Ang ely 24, avenue des Diables Bleus, Nice Cedex 06357, France
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 19 September 2014
We introduce the papers of the JAS Special Issue: Radiocarbon Dating that were presented at the First Frison Institute Symposium at the 2013 Society for American Archaeology annual meeting. Papers here fall into two categories that reflect two growing trends in archaeology: the use of summed probability distributions as measures of human population, and the use of Bayesian statistics to refine radiocarbon age estimates. While caution is required, these two methods combined offer archaeology the possibility of tracking change in the size of human populations through time and across space. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Radiocarbon dating Summed probability distributions Bayesian statistics
Archaeology's great strengths are space and time. Although it cannot reconstruct the past in the detail that ethnographers can do with living peoples, archaeology can observe cultural/historical processes over long reaches of time and wide reaches of space. Therefore, large historical narratives are an important contribution that archaeology makes to anthropology. These began in the 19th century, before archaeology was a formal discipline and when we actually knew little about the human past; consequently, they were often ill-informed. It is only in recent decades, however, that archaeologists have amassed enough data (especially through CRM) and have the technology and analytical techniques to create syntheses on a firm empirical foundation, in greater detail, with the ability to interrogate conclusions with analytical rigour. In this regard, one of the potentially most useful pieces of data is radiocarbon dates. Radiocarbon dating has been around now for more than half a century, and tens of thousands of 14C dates exist. Though limited to the last 45,000 years of prehistory, they are a common standard across that time range, and thus can be used to compare the archaeologies of different regions and cultural forms. Radiocarbon dates will be an important component of research as archaeology moves into the era of Big Data. Several 14C databases now exist, including the S2AGES and INQUA databases in Europe,
* Corresponding author. Tel.: þ1 307 766 3135; fax: þ1 307 766 2473. E-mail addresses: [email protected] (R.L. Kelly), nicolas.naudinot@cepam. cnrs.fr (N. Naudinot). 1 Tel.: þ33 (0)4 89 88 15 03. http://dx.doi.org/10.1016/j.jas.2014.09.004 0305-4403/© 2014 Elsevier Ltd. All rights reserved.
Australia's AustArch 1 and 2 (Williams et al., 2008), the early Holocene Brazil database (Bueno et al., 2013), and the Canadian Archaeological Radiocarbon Database, now housed at the University of British Columbia (Gajewski et al., 2011). Seven of the following eight papers in this volume were presented at the First Frison Institute Symposium, held at the 2013 Society for American Archaeology annual meeting; Contreras and Meadows was added later. The goal of the Frison Institute symposia is to bring together a group of international researchers working on a topic of broad interest to the discipline of archaeology. These papers show two recent lines of research that make use of radiocarbon dates and that are becoming more common globally. The first of these uses “dates as data,” to borrow John Rick's (1987) phrasing. Stated simply, the idea is that sufficiently large samples of 14C dates, or summed probability distributions produced through calibration might provide the ability to track relative changes in human populations through time and across space (Armit et al., 2013; Bocquet-Appel et al., 2009; Buchanan et al., 2008, 2011; Dolukhanov et al., 2002, 2005; Gamble et al., 2005; Graf, 2009; Oinonen et al., 2010; Fiedel and Kuzman, 2007; Louderback et al., 2011; Mullen, 2012; Munoz et al., 2010; Onkamo et al., 2012; Pesonen, 2002; Perez et al., 2010; Railey et al., 2009; Riede, 2008, 2009; Riede and Edinborough, 2012; Riede et al., 2009; Shennan and Edinborough, 2007; Shennan et al., 2013; Steele, 2010; Tallavaara et al., 2010; Turney et al., 2006). Some research using 14C dates in this fashion, sometimes with Bayesian statistical models, suggests close relationships between climate and population (e.g., Riede, 2008, 2009; Riede and Edinborough,
R.L. Kelly, N. Naudinot / Journal of Archaeological Science 52 (2014) 546e548
€, 2011) until, perhaps, agriculture enters 2012; Tallavaar and Seppa the economy (Shennan et al., 2013; Tallavaara and Sepp€ a, 2011). Research in Wyoming shows a tight correlation between population and climate over a span of 10,000 years (Kelly et al., 2013). This approach is not without its critics. Researcher bias (e.g., Ballenger and Mabrey, 2011) can produce “false” peaks and valleys, as can calibration itself (e.g., Williams, 2012; Bamforth and Grund, 2012; see also Armit et al., 2013; Buchanan et al., 2011); and taphonomic loss (e.g., Surovell et al., 2009) can bias older dates in favour of younger ones. We are uncertain as to how large a sample we need to produce a valid distribution (Williams, 2012), what date density is needed to reliably track populations changing at different rates (Contreras and Meadows, 2014), whether we can treat the ratio of population: date production as the same throughout a sequence (Peros et al., 2010), or whether highly mobile societies produce more sites and hence more dates than sedentary peoples (Tallavaara et al., 2010; Naudinot et al., 2014) or alternatively whether sites of nomadic peoples are more ephemeral and thus undercounted relative to larger, more visible sites of sedentary villages. In any case, this approach is only as good as the data used, and so analysts must take care in “cleaning” a dataset and to consider the context of each date. While caution is required, this approach may move archaeology toward a relative and comparative measure of human population through time and across space. A second trend is one that has characterized archaeology's entire history: the refinement of age estimates. In the 1950s, radiocarbon dating freed many archaeologists from the difficult task of calculating a site's absolute age, a task often based on various and sometimes dubious assumptions about rates and/or directions of change in artifact style, or rates of sediment accumulation. By vastly reducing the amount of organic material needed (5e10 mg) AMS dating in the 1970s improved on radiometric dating by making it possible to date objects that previously could not be dated and to reduce the standard error (<40 years is now routine). Then calibration, widely available in the 1980s and now easily performed on-line, allowed the refinement of radiometric ages even further, and the algorithm is continually upgraded (e.g., Reimer et al., 2013). We are now adding to this trend the use of Bayesian statistics, which use a radiocarbon date's contextual information to refine the age of an event, or to estimate the age of events that have no directly associated ages (e.g., a living surface sandwiched between dated surfaces). Bayesian statistical approaches were introduced to archaeology some 30 years ago (Bronk Ramsey, 1995; Buck et al., 1996; Bayliss et al., 1999), but they have been more commonly employed in Britain and Europe than in the New World; that, however, is changing, as papers here demonstrate (see also, e.g., Culleton et al., 2012; Inomata et al., 2013; Kennett et al., 2011). These trends both deal with time, but are different in their perspective: the first creates coarser patterns that incorporate wide reaches of space and time (to continents and 10,000 years or more), while the second seeks extremely fine-grained temporal analysis (down to a decade or less) of occupational patterns at one or a few sites. It is best not to think of these approaches as opposed, however, but as complementary: the former can create hypotheses that can then be tested at carefully excavated and dated stratified sites. We offer these papers, then, to help move archaeologists toward our goal of documenting and explaining the temporal and spatial patterns that archaeological data are best suited to document and explain. References Armit, I., Swindles, G.T., Becker, K., 2013. From dates to demography in later prehistoric Ireland? Experimental approaches to the meta-analysis of large 14C data-sets. J. Archaeol. Sci. 40, 433e438.
547
Ballenger, J., Mabrey, J.B., 2011. Temporal frequency distributions of alluvium in the American Southwest: taphonomic, paleohydraulic, and demographic implications. J. Archaeol. Sci. 38, 1314e1325. Bamforth, D.B., Grund, B., 2012. Radiocarbon calibration curves, summed probability distributions, and early paleoindian population trends in North America. J. Archaeol. Sci. 39, 1768e1774. Bayliss, A., Groves, C., McCormac, G., Baillie, M., Brown, D., Brennand, M., 1999. Precise dating of the Norfolk timber circle. Nature 402, 479. Bocquet-Appel, J.-P., Naji, S., Vander Linden, M., Kozlowski, J.K., 2009. Detection of diffusion and contact zones of early farming in Europe from the space-time distribution of 14C dates. J. Archaeol. Sci. 36, 807e820. Bronk Ramsey, C., 1995. Radiocarbon calibration and analysis of stratigraphy: the OxCal program. Radiocarbon 37, 425e430. Buchanan, B., Collard, M., Edinborough, K., 2008. Paleoindian demography and the extraterrestrial impact hypothesis. Proc. Natl. Acad. Sci. 105, 11651e11654. Buchanan, B., Hamilton, M., Edinborough, K., O'Brien, M.J., Collard, M., 2011. A comment on Steele's (2010) “Radiocarbon dates as data: quantitative strategies for estimating colonization front speeds and event densities.” J. Archaeol. Sci. 38, 2116e2122. Buck, C.E., Cavanagh, W.G., Litton, C.D., 1996. Bayesian Approach to Interpreting Archaeological Data. Wiley, Chichester. Bueno, L., Prates, L., Politis, G.G., Steele, J., 2013. A Late Pleistocene/early Holocene archaeological 14C database for South America and the Isthmus of Panama: palaeoenvironmental contexts and demographic interpretations. Quat. Int. 301, 1e2. Contreras, D.A., Meadows, J., 2014. Summed radiocarbon calibrations as a population proxy: a critical evaluation using a realistic simulation approach. J. Archaeol. Sci. 52, 591e608. Culleton, B.J., Prufer, K.M., Kennett, D.J., 2012. A Bayesian AMS 14C chronology of the Classic Maya Center of Uxbenk a, Belize. J. Archaeol. Sci. 39, 1572e1586. Dolukhanov, P.M., Shukurov, A.M., Tarasov, P.E., Zaitseva, G.I., 2002. Colonization of Northern Eurasia by modern humans: radiocarbon chronology and environment. J. Archaeol. Sci. 29, 593e606. Dolukhanov, P.M., Shukurov, A., Gronenborn, D., Sokoloff, D., Timofeev, V., Zaitseva, G., 2005. The chronology of Neolithic dispersal in Central and Eastern Europe. J. Archaeol. Sci. 32, 1441e1458. Fiedel, S.J., Kuzmin, Y.V., 2007. Radiocarbon date frequency as an index of intensity of Paleolithic occupation of Siberia: Did humans react predictably to climate oscillations? Radiocarbon 49, 741e756. Gajewski, K., Munoz, S., Peros, M., Viau, A., Morlan, R., Betts, M., 2011. The Canadian archaeological radiocarbon database (CARD): archaeological 14C dates in North American and their paleoenvironmental context. Radiocarb 53, 371e394. Gamble, C., Davies, W., Pettitt, P., Hazelwood, L., Richards, M., 2005. The archaeological and genetic foundations of the European population during the late glacial: implications for ‘Agricultural Thinking’. Camb. Archaeol. J. 15, 193e223. Graf, K.E., 2009. ‘‘The Good, the Bad, and the Ugly’’: evaluating the radiocarbon chronology of the middle and late Upper Paleolithic in the Enisei River valley, south-central Siberia. J. Archaeol. Sci. 36, 694e707. Inomata, T., Triadan, D., Aoyama, K., Castillo, V., Yonenbu, H., 2013. Early Ceremonial Constructions at Ceibal, Guatemala, and the Origins of Lowland Maya Civilization. Science 340, 467e471. Kelly, R.L., Surovell, T., Shuman, B., Smith, G., 2013. A continuous climatic impact on holocene human population in the Rocky Mountains. Proc. Natl. Acad. Sci. 110, 443e447. Kennett, D.J., Culleton, B.J., Voorhies, B., Southon, J.R., 2011. Bayesian Analysis of high-precision AMS 14C dates from a prehistoric Mexican shellmound. Radiocarbon 53, 245e259. Louderback, L.A., Grayson, D.K., Llobera, M., 2011. Middle-Holocene climates and human population densities in the Great Basin, western USA. Holocene 21, 366e373. Mullen, P.O., 2012. An archaeological test of the effects of the White River ash eruptions. Arct. Anthropol. 49, 35e44. Munoz, S.E., Gajewski, K., Peros, M.C., 2010. Synchronous environmental and cultural change in the prehistory of the northeastern. U. S. Proc. Natl. Acad. Sci. 107, 22008e220013. Naudinot, N., Tomasso, A., Tozzi, C., Peresani, M., 2014. Changes in mobility patterns as a factor of 14C date density variation in the Late Epigravettian of Northern Italy and Southeastern France. J. Archaeol. Sci. 52, 578e590. Oinonen, M., Pesonen, P., Tallavaara, M., 2010. Archaeological radiocarbon dates for studying the population history in eastern Fennoscandia. Radiocarbon 52, 393e407. Onkamo, P., Kammonen, J., Pesonen, P., Sundell, T., Moltchanova, E., Oinonen, M., Haimila, M., Arjas, E., 2012. Bayesian spatiotemporal analysis of radiocarbon dates from eastern Fennoscandia. Radiocarb 54, 1e11. Peros, M.C., Munoz, S.E., Gajewski, K., Viau, A.E., 2010. Prehistoric demography of North America inferred from radiocarbon data. J. Archaeol. Sci. 37, 656e664. Pesonen, P., 2002. Semisubterranean houses in Finlandda review. In: Ranta, H. (Ed.), Huts & Houses. Stone Age & Early Metal Age Buildings in Finland. National Board of Antiquities, Helsinki, pp. 9e41. Railey, J.A., Rissetto, J., Bandy, M., 2009. Synthesis of Excavation Data for the Permian Basin Mitigation Program. Submitted to Bureau of Land Management, Carlsbad Field Office. SWCA Environmental Consultants. BLM Contract Number L08PC90396. Reimer, P.J., Bard, E., Bayliss, A., Beck, J.W., Blackwell, P.G., Bronk Ramsey, C., Buck, C.E., Cheng, H., Edwards, R.L., Friedrich, M., Grootes, P.M., Guilderson, T.P.,
548
R.L. Kelly, N. Naudinot / Journal of Archaeological Science 52 (2014) 546e548
, C., Heaton, T.J., Hoffmann, D.L., Hogg, A.G., Haflidason, H., Hajdas, I., Hatte Hughen, K.A., Kaiser, K.F., Kromer, B., Manning, S.W., Niu, M., Reimer, R.W., Richards, D.A., Scott, E.M., Southon, J.R., Staff, R.A., Turney, C.S.M., van der Plicht, J., 2013. IntCal13 and Marine13 radiocarbon age calibration curves 0e50,000 years cal BP. Radiocarb 55, 1869e1887. Rick, J.W., 1987. Dates as data: an examination of the Peruvian preceramic radiocarbon record. Am. Antiq. 52, 55e73. Riede, F., 2008. The Laacher See-eruption (12,920 BP) and material culture change at the end of the Allerød in Northern Europe. J. Archaeol. Sci. 35, 591e599. Riede, F., 2009. Climate and demography in early prehistory: using calibrated 14C dates as population proxies. Hum. Biol. 81, 309e337. Riede, F., Edinborough, K., 2012. Bayesian radiocarbon models for the cultural transition during the Allerød in southern Scandinavia. J. Archaeol. Sci. 39, 744e756. Riede, F., Edinborough, K., Thomas, M., 2009. Tracking Mesolithic demography in time and space and its implications for explanations of culture change. In: , P., Van Strydonck, M., Sergant, J. (Eds.), Chronology & Evolution in Crombe the Mesolithic of N(W) Europe. Cambridge Scholars, Newcastle, U.K, pp. 181e199. Shennan, S., Edinborough, K., 2007. Prehistoric population history: from the Late Glacial to the Late Neolithic in Central and Northern Europe. J. Archaeol. Sci. 34, 1339e1345.
Shennan, S., Downey, S.S., Timpson, A., Edinborough, K., Colledge, S., Kerig, T., Manning, K., Thomas, M., 2013. Regional population collapse followed initial agriculture booms in mid-Holocene Europe. Nat. Commun. 4, 2486. http:// dx.doi.org/10.1038/ncomms3486. www.nature.com/naturecommunications. Steele, J., 2010. Radiocarbon dates as data: quantitative strategies for estimating colonization front speeds and event densities. J. Archaeol. Sci. 37, 2017e2030. Surovell, T., Finley, J., Smith, G., Brantingham, P.J., Kelly, R., 2009. Correcting temporal frequency distributions for taphonomic bias. J. Archaeol. Sci. 36, 1715e1724. Tallavaara, M., Sepp€ a, H., 2011. Did the mid-Holocene environmental changes cause the boom and bust of hunter-gatherer population size in eastern Fennoscandia? Holocene 22, 215e225. Tallavaara, M., Pesonen, P., Oinonen, M., 2010. Prehistoric population history in eastern Fennoscandia. J. Archaeol. Sci. 37, 251e260. Williams, A.N., 2012. The use of summed radiocarbon probability distributions in archaeology: a review of methods. J. Archaeol. Sci. 39, 578e589. Williams, A.N., Smith, M.A., Turney, C.S.M., Cupper, M., 2008. Austarch1: a database of 14C and luminescence ages from archaeological sites in the Australian arid zone. Aust. Archaeol. 66, 99.
Contents lists available at ScienceDirect
Journal of Archaeological Science journal homepage: http://www.elsevier.com/locate/jas
Introduction to the Frison Institute Symposium on radiocarbon dating applications Robert L. Kelly a, *, Nicolas Naudinot b, 1 a b
Dept. of Anthropology, University of Wyoming, Laramie WY 82071, USA Universit e de Nice Sophia Antipolis, Campus Saint-Jean-d'Ang ely 24, avenue des Diables Bleus, Nice Cedex 06357, France
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 19 September 2014
We introduce the papers of the JAS Special Issue: Radiocarbon Dating that were presented at the First Frison Institute Symposium at the 2013 Society for American Archaeology annual meeting. Papers here fall into two categories that reflect two growing trends in archaeology: the use of summed probability distributions as measures of human population, and the use of Bayesian statistics to refine radiocarbon age estimates. While caution is required, these two methods combined offer archaeology the possibility of tracking change in the size of human populations through time and across space. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Radiocarbon dating Summed probability distributions Bayesian statistics
Archaeology's great strengths are space and time. Although it cannot reconstruct the past in the detail that ethnographers can do with living peoples, archaeology can observe cultural/historical processes over long reaches of time and wide reaches of space. Therefore, large historical narratives are an important contribution that archaeology makes to anthropology. These began in the 19th century, before archaeology was a formal discipline and when we actually knew little about the human past; consequently, they were often ill-informed. It is only in recent decades, however, that archaeologists have amassed enough data (especially through CRM) and have the technology and analytical techniques to create syntheses on a firm empirical foundation, in greater detail, with the ability to interrogate conclusions with analytical rigour. In this regard, one of the potentially most useful pieces of data is radiocarbon dates. Radiocarbon dating has been around now for more than half a century, and tens of thousands of 14C dates exist. Though limited to the last 45,000 years of prehistory, they are a common standard across that time range, and thus can be used to compare the archaeologies of different regions and cultural forms. Radiocarbon dates will be an important component of research as archaeology moves into the era of Big Data. Several 14C databases now exist, including the S2AGES and INQUA databases in Europe,
* Corresponding author. Tel.: þ1 307 766 3135; fax: þ1 307 766 2473. E-mail addresses: [email protected] (R.L. Kelly), nicolas.naudinot@cepam. cnrs.fr (N. Naudinot). 1 Tel.: þ33 (0)4 89 88 15 03. http://dx.doi.org/10.1016/j.jas.2014.09.004 0305-4403/© 2014 Elsevier Ltd. All rights reserved.
Australia's AustArch 1 and 2 (Williams et al., 2008), the early Holocene Brazil database (Bueno et al., 2013), and the Canadian Archaeological Radiocarbon Database, now housed at the University of British Columbia (Gajewski et al., 2011). Seven of the following eight papers in this volume were presented at the First Frison Institute Symposium, held at the 2013 Society for American Archaeology annual meeting; Contreras and Meadows was added later. The goal of the Frison Institute symposia is to bring together a group of international researchers working on a topic of broad interest to the discipline of archaeology. These papers show two recent lines of research that make use of radiocarbon dates and that are becoming more common globally. The first of these uses “dates as data,” to borrow John Rick's (1987) phrasing. Stated simply, the idea is that sufficiently large samples of 14C dates, or summed probability distributions produced through calibration might provide the ability to track relative changes in human populations through time and across space (Armit et al., 2013; Bocquet-Appel et al., 2009; Buchanan et al., 2008, 2011; Dolukhanov et al., 2002, 2005; Gamble et al., 2005; Graf, 2009; Oinonen et al., 2010; Fiedel and Kuzman, 2007; Louderback et al., 2011; Mullen, 2012; Munoz et al., 2010; Onkamo et al., 2012; Pesonen, 2002; Perez et al., 2010; Railey et al., 2009; Riede, 2008, 2009; Riede and Edinborough, 2012; Riede et al., 2009; Shennan and Edinborough, 2007; Shennan et al., 2013; Steele, 2010; Tallavaara et al., 2010; Turney et al., 2006). Some research using 14C dates in this fashion, sometimes with Bayesian statistical models, suggests close relationships between climate and population (e.g., Riede, 2008, 2009; Riede and Edinborough,
R.L. Kelly, N. Naudinot / Journal of Archaeological Science 52 (2014) 546e548
€, 2011) until, perhaps, agriculture enters 2012; Tallavaar and Seppa the economy (Shennan et al., 2013; Tallavaara and Sepp€ a, 2011). Research in Wyoming shows a tight correlation between population and climate over a span of 10,000 years (Kelly et al., 2013). This approach is not without its critics. Researcher bias (e.g., Ballenger and Mabrey, 2011) can produce “false” peaks and valleys, as can calibration itself (e.g., Williams, 2012; Bamforth and Grund, 2012; see also Armit et al., 2013; Buchanan et al., 2011); and taphonomic loss (e.g., Surovell et al., 2009) can bias older dates in favour of younger ones. We are uncertain as to how large a sample we need to produce a valid distribution (Williams, 2012), what date density is needed to reliably track populations changing at different rates (Contreras and Meadows, 2014), whether we can treat the ratio of population: date production as the same throughout a sequence (Peros et al., 2010), or whether highly mobile societies produce more sites and hence more dates than sedentary peoples (Tallavaara et al., 2010; Naudinot et al., 2014) or alternatively whether sites of nomadic peoples are more ephemeral and thus undercounted relative to larger, more visible sites of sedentary villages. In any case, this approach is only as good as the data used, and so analysts must take care in “cleaning” a dataset and to consider the context of each date. While caution is required, this approach may move archaeology toward a relative and comparative measure of human population through time and across space. A second trend is one that has characterized archaeology's entire history: the refinement of age estimates. In the 1950s, radiocarbon dating freed many archaeologists from the difficult task of calculating a site's absolute age, a task often based on various and sometimes dubious assumptions about rates and/or directions of change in artifact style, or rates of sediment accumulation. By vastly reducing the amount of organic material needed (5e10 mg) AMS dating in the 1970s improved on radiometric dating by making it possible to date objects that previously could not be dated and to reduce the standard error (<40 years is now routine). Then calibration, widely available in the 1980s and now easily performed on-line, allowed the refinement of radiometric ages even further, and the algorithm is continually upgraded (e.g., Reimer et al., 2013). We are now adding to this trend the use of Bayesian statistics, which use a radiocarbon date's contextual information to refine the age of an event, or to estimate the age of events that have no directly associated ages (e.g., a living surface sandwiched between dated surfaces). Bayesian statistical approaches were introduced to archaeology some 30 years ago (Bronk Ramsey, 1995; Buck et al., 1996; Bayliss et al., 1999), but they have been more commonly employed in Britain and Europe than in the New World; that, however, is changing, as papers here demonstrate (see also, e.g., Culleton et al., 2012; Inomata et al., 2013; Kennett et al., 2011). These trends both deal with time, but are different in their perspective: the first creates coarser patterns that incorporate wide reaches of space and time (to continents and 10,000 years or more), while the second seeks extremely fine-grained temporal analysis (down to a decade or less) of occupational patterns at one or a few sites. It is best not to think of these approaches as opposed, however, but as complementary: the former can create hypotheses that can then be tested at carefully excavated and dated stratified sites. We offer these papers, then, to help move archaeologists toward our goal of documenting and explaining the temporal and spatial patterns that archaeological data are best suited to document and explain. References Armit, I., Swindles, G.T., Becker, K., 2013. From dates to demography in later prehistoric Ireland? Experimental approaches to the meta-analysis of large 14C data-sets. J. Archaeol. Sci. 40, 433e438.
547
Ballenger, J., Mabrey, J.B., 2011. Temporal frequency distributions of alluvium in the American Southwest: taphonomic, paleohydraulic, and demographic implications. J. Archaeol. Sci. 38, 1314e1325. Bamforth, D.B., Grund, B., 2012. Radiocarbon calibration curves, summed probability distributions, and early paleoindian population trends in North America. J. Archaeol. Sci. 39, 1768e1774. Bayliss, A., Groves, C., McCormac, G., Baillie, M., Brown, D., Brennand, M., 1999. Precise dating of the Norfolk timber circle. Nature 402, 479. Bocquet-Appel, J.-P., Naji, S., Vander Linden, M., Kozlowski, J.K., 2009. Detection of diffusion and contact zones of early farming in Europe from the space-time distribution of 14C dates. J. Archaeol. Sci. 36, 807e820. Bronk Ramsey, C., 1995. Radiocarbon calibration and analysis of stratigraphy: the OxCal program. Radiocarbon 37, 425e430. Buchanan, B., Collard, M., Edinborough, K., 2008. Paleoindian demography and the extraterrestrial impact hypothesis. Proc. Natl. Acad. Sci. 105, 11651e11654. Buchanan, B., Hamilton, M., Edinborough, K., O'Brien, M.J., Collard, M., 2011. A comment on Steele's (2010) “Radiocarbon dates as data: quantitative strategies for estimating colonization front speeds and event densities.” J. Archaeol. Sci. 38, 2116e2122. Buck, C.E., Cavanagh, W.G., Litton, C.D., 1996. Bayesian Approach to Interpreting Archaeological Data. Wiley, Chichester. Bueno, L., Prates, L., Politis, G.G., Steele, J., 2013. A Late Pleistocene/early Holocene archaeological 14C database for South America and the Isthmus of Panama: palaeoenvironmental contexts and demographic interpretations. Quat. Int. 301, 1e2. Contreras, D.A., Meadows, J., 2014. Summed radiocarbon calibrations as a population proxy: a critical evaluation using a realistic simulation approach. J. Archaeol. Sci. 52, 591e608. Culleton, B.J., Prufer, K.M., Kennett, D.J., 2012. A Bayesian AMS 14C chronology of the Classic Maya Center of Uxbenk a, Belize. J. Archaeol. Sci. 39, 1572e1586. Dolukhanov, P.M., Shukurov, A.M., Tarasov, P.E., Zaitseva, G.I., 2002. Colonization of Northern Eurasia by modern humans: radiocarbon chronology and environment. J. Archaeol. Sci. 29, 593e606. Dolukhanov, P.M., Shukurov, A., Gronenborn, D., Sokoloff, D., Timofeev, V., Zaitseva, G., 2005. The chronology of Neolithic dispersal in Central and Eastern Europe. J. Archaeol. Sci. 32, 1441e1458. Fiedel, S.J., Kuzmin, Y.V., 2007. Radiocarbon date frequency as an index of intensity of Paleolithic occupation of Siberia: Did humans react predictably to climate oscillations? Radiocarbon 49, 741e756. Gajewski, K., Munoz, S., Peros, M., Viau, A., Morlan, R., Betts, M., 2011. The Canadian archaeological radiocarbon database (CARD): archaeological 14C dates in North American and their paleoenvironmental context. Radiocarb 53, 371e394. Gamble, C., Davies, W., Pettitt, P., Hazelwood, L., Richards, M., 2005. The archaeological and genetic foundations of the European population during the late glacial: implications for ‘Agricultural Thinking’. Camb. Archaeol. J. 15, 193e223. Graf, K.E., 2009. ‘‘The Good, the Bad, and the Ugly’’: evaluating the radiocarbon chronology of the middle and late Upper Paleolithic in the Enisei River valley, south-central Siberia. J. Archaeol. Sci. 36, 694e707. Inomata, T., Triadan, D., Aoyama, K., Castillo, V., Yonenbu, H., 2013. Early Ceremonial Constructions at Ceibal, Guatemala, and the Origins of Lowland Maya Civilization. Science 340, 467e471. Kelly, R.L., Surovell, T., Shuman, B., Smith, G., 2013. A continuous climatic impact on holocene human population in the Rocky Mountains. Proc. Natl. Acad. Sci. 110, 443e447. Kennett, D.J., Culleton, B.J., Voorhies, B., Southon, J.R., 2011. Bayesian Analysis of high-precision AMS 14C dates from a prehistoric Mexican shellmound. Radiocarbon 53, 245e259. Louderback, L.A., Grayson, D.K., Llobera, M., 2011. Middle-Holocene climates and human population densities in the Great Basin, western USA. Holocene 21, 366e373. Mullen, P.O., 2012. An archaeological test of the effects of the White River ash eruptions. Arct. Anthropol. 49, 35e44. Munoz, S.E., Gajewski, K., Peros, M.C., 2010. Synchronous environmental and cultural change in the prehistory of the northeastern. U. S. Proc. Natl. Acad. Sci. 107, 22008e220013. Naudinot, N., Tomasso, A., Tozzi, C., Peresani, M., 2014. Changes in mobility patterns as a factor of 14C date density variation in the Late Epigravettian of Northern Italy and Southeastern France. J. Archaeol. Sci. 52, 578e590. Oinonen, M., Pesonen, P., Tallavaara, M., 2010. Archaeological radiocarbon dates for studying the population history in eastern Fennoscandia. Radiocarbon 52, 393e407. Onkamo, P., Kammonen, J., Pesonen, P., Sundell, T., Moltchanova, E., Oinonen, M., Haimila, M., Arjas, E., 2012. Bayesian spatiotemporal analysis of radiocarbon dates from eastern Fennoscandia. Radiocarb 54, 1e11. Peros, M.C., Munoz, S.E., Gajewski, K., Viau, A.E., 2010. Prehistoric demography of North America inferred from radiocarbon data. J. Archaeol. Sci. 37, 656e664. Pesonen, P., 2002. Semisubterranean houses in Finlandda review. In: Ranta, H. (Ed.), Huts & Houses. Stone Age & Early Metal Age Buildings in Finland. National Board of Antiquities, Helsinki, pp. 9e41. Railey, J.A., Rissetto, J., Bandy, M., 2009. Synthesis of Excavation Data for the Permian Basin Mitigation Program. Submitted to Bureau of Land Management, Carlsbad Field Office. SWCA Environmental Consultants. BLM Contract Number L08PC90396. Reimer, P.J., Bard, E., Bayliss, A., Beck, J.W., Blackwell, P.G., Bronk Ramsey, C., Buck, C.E., Cheng, H., Edwards, R.L., Friedrich, M., Grootes, P.M., Guilderson, T.P.,
548
R.L. Kelly, N. Naudinot / Journal of Archaeological Science 52 (2014) 546e548
, C., Heaton, T.J., Hoffmann, D.L., Hogg, A.G., Haflidason, H., Hajdas, I., Hatte Hughen, K.A., Kaiser, K.F., Kromer, B., Manning, S.W., Niu, M., Reimer, R.W., Richards, D.A., Scott, E.M., Southon, J.R., Staff, R.A., Turney, C.S.M., van der Plicht, J., 2013. IntCal13 and Marine13 radiocarbon age calibration curves 0e50,000 years cal BP. Radiocarb 55, 1869e1887. Rick, J.W., 1987. Dates as data: an examination of the Peruvian preceramic radiocarbon record. Am. Antiq. 52, 55e73. Riede, F., 2008. The Laacher See-eruption (12,920 BP) and material culture change at the end of the Allerød in Northern Europe. J. Archaeol. Sci. 35, 591e599. Riede, F., 2009. Climate and demography in early prehistory: using calibrated 14C dates as population proxies. Hum. Biol. 81, 309e337. Riede, F., Edinborough, K., 2012. Bayesian radiocarbon models for the cultural transition during the Allerød in southern Scandinavia. J. Archaeol. Sci. 39, 744e756. Riede, F., Edinborough, K., Thomas, M., 2009. Tracking Mesolithic demography in time and space and its implications for explanations of culture change. In: , P., Van Strydonck, M., Sergant, J. (Eds.), Chronology & Evolution in Crombe the Mesolithic of N(W) Europe. Cambridge Scholars, Newcastle, U.K, pp. 181e199. Shennan, S., Edinborough, K., 2007. Prehistoric population history: from the Late Glacial to the Late Neolithic in Central and Northern Europe. J. Archaeol. Sci. 34, 1339e1345.
Shennan, S., Downey, S.S., Timpson, A., Edinborough, K., Colledge, S., Kerig, T., Manning, K., Thomas, M., 2013. Regional population collapse followed initial agriculture booms in mid-Holocene Europe. Nat. Commun. 4, 2486. http:// dx.doi.org/10.1038/ncomms3486. www.nature.com/naturecommunications. Steele, J., 2010. Radiocarbon dates as data: quantitative strategies for estimating colonization front speeds and event densities. J. Archaeol. Sci. 37, 2017e2030. Surovell, T., Finley, J., Smith, G., Brantingham, P.J., Kelly, R., 2009. Correcting temporal frequency distributions for taphonomic bias. J. Archaeol. Sci. 36, 1715e1724. Tallavaara, M., Sepp€ a, H., 2011. Did the mid-Holocene environmental changes cause the boom and bust of hunter-gatherer population size in eastern Fennoscandia? Holocene 22, 215e225. Tallavaara, M., Pesonen, P., Oinonen, M., 2010. Prehistoric population history in eastern Fennoscandia. J. Archaeol. Sci. 37, 251e260. Williams, A.N., 2012. The use of summed radiocarbon probability distributions in archaeology: a review of methods. J. Archaeol. Sci. 39, 578e589. Williams, A.N., Smith, M.A., Turney, C.S.M., Cupper, M., 2008. Austarch1: a database of 14C and luminescence ages from archaeological sites in the Australian arid zone. Aust. Archaeol. 66, 99.