Dr. Chris Carleton
The effects of modern climate change will be felt for centuries to come. Planning for that future right now is very difficult, however. We do not know how human societies respond to climate change over the long term. Modern and historically recent cases cannot provide us with a solid basis for making predications about the future because modern climate change has not been going on long enough to see its full effects. Instead, we need to look to the archaeological record for examples of long-term human responses to climate change.
Despite more than a century of effort, though, archaeologists have made limited progress in understanding past human-environment dynamics. Archaeological and palaeoenvironmental datasets have improved markedly, but attempts to link those records have so far been unconvincing. The primary reason for this is a lack of appropriate quantitative tools. Archaeological and palaeoenvironmental data contain idiosyncrasies—namely temporal autocorrelation and chronological uncertainty—that undermine statistical methods. Given the seriousness of modern climate change, we need to rectify this situation.
In this dissertation, I lay the groundwork for developing a quantitative toolkit for analyzing long-term human-environment dynamics. The dissertation is comprised of four studies involving time-series methods. The first two look at the impact of climate changes on the Classic Maya using two types of time-series analysis, and the last two use simulations to probe the limits of these methods. Together, the four studies demonstrate that the idiosyncrasies of archaeological and palaeoenvironmental data create challenges for quantitative analyses. Reviewing the studies, I identify the main methodological challenges and sketch out some potential solutions, illuminating a path for future methodological development.
Collapse of the Pre-Pottery Neolithic ‘B’ in Southwest Asia,” Department of Anthropology, Trent University, 2010
In an effort to explore the nature of the end of the Late Pre-Pottery Neolithic B (ca. late 9th millennium cal. BP), four major categories of archaeological data from Çatalhöyük, located in south-central Turkey, and a reconstruction of palaeoenvironmental global climatic conditions, were modelled using statistical and analytical techniques. The archaeological data was considered to be proxy evidence for latent complex cultural systems, and those latent structures were identified within the dataset using Principle Axis Factoring (PAF). PAF was used successfully for modelling the architectural, lithic, and faunal datasets. A single vector of the botanical dataset was selected for modelling based on an examination of that data, which revealed that much of the information within the dataset could be captured by one vector alone. In order to make use of the palaeoclimatic data, Time Series Analysis was used to decompose the signal in the data into its constituents so that the important trends in the data could be isolated. Each of the latent systems constructed with the PAF model, and the vector of proxy data from the botanical dataset, were interpreted from the perspective of Evolutionary Archaeology and Complexity Theory. This research demonstrates that archaeological data can serve as a proxy for extinct, latent cultural systems and that synchronicity between various socionatural and cultural systems can be quantitatively demonstrated. This indicates that there is potential for retrodictive hypothesis testing given data from the archaeological record.
Carleton, W., Campbell, D., and Collard, M. 2017. Increasing temperature exacerbated Classic Maya conflict over the long term. Quaternary Science Reviews, 163:209–218.
Carleton, W., Cheong, K., Savage, D., Barry, J., Conolly, J., Iannone, G. 2017. A Comprehensive Test of the Locally-Adaptive Model of Archaeological Potential (LAMAP). Journal of Archaeological Science: Reports, 11:59–68.
Carleton, W., Campbell, D., and Collard, M. 2014. A reassessment of the impact of drought cycles on the Classic Maya. Quaternary Science Reviews 105:151–161.
Carleton, W., Conolly, J., and Collard, M. 2013. Corporate kin-groups, social memory, and “history houses”? A quantitative test of recent reconstructions of social organization and building function at Çatalhöyük during the PPNB. Journal of Archaeological Science, 40(4):1816–1822.
Carleton, W., Conolly J., and Iannone G. 2012. A Locally-Adaptive Model of Archaeological Potential (LAMAP). Journal of Archaeological Science, 39(11):3371–3385.
Carleton, W., Campbell, D., Collard, M., 2017. “Chronological uncertainty severely undermines our ability to identify cycles in archaeological and palaeoenvironmental records” paper presented at the 82nd Annual Meeting of the Society for American Archaeology in Vancouver, BC.
Kong, C., W. Carleton, D. Savage, J. Conolly, G. Ianonne, J. Barry, 2015. “Testing a Locally-Adaptive Model of Archaeological Potential (LAMAP) to Assess Ancient Maya Settlement Location and Density in Belize’s North Vaca Plateau,” paper presented at the 80th Annual Meeting of the Society for American Archaeology in San Francisco, CA.
Carleton, W., M. Collard, and D. Campbell. 2015. “Parched and prickly or hot and bothered? Comparing drought and temperature as potential drivers of Classic Maya Conflict,” paper presented at the 80th Annual Meeting of the Society for American Archaeology in San Francisco, CA.
Carleton, W., M. Collard, and D. Campbell. 2015. “Parched and prickly or hot and bothered? Comparing drought and temperature as potential drivers of Classic Maya Conflict,” paper presented at the 4th annual Human Evolutionary Studies Program Symposium, Burnaby, BC.
Carleton, W., M. Collard, D. Campbell, and J. Munson. 2013. "Debating drought cycles and their influence on Maya society," paper presented at the 78th annual meeting of the Society for American Archaeology, Honolulu, HI.
Carleton, W., J. Conolly, and M. Collard. 2012. "Socioeconomic structure at Catalhoyuk: debating corporate kin-groups," paper presented at the 77th annual meeting of the Society for American Archaeology, Memphis, TN.