d’Alpoim Guedes, Jade, and R. Kyle Bocinsky
2018 Climate change stimulated agricultural innovation and exchange across Asia. Science Advances 31 Oct 2018, 4(10), eaar4491. DOI: 10.1126/sciadv.aar4491
Ancient farmers experienced climate change at the local level through variations in the yields of their staple crops. However, archaeologists have had difficulty in determining where, when, and how changes in climate affected ancient farmers. We model how several key transitions in temperature affected the productivity of six grain crops across Eurasia. Cooling events between 3750 and 3000 cal. BP lead humans in parts of the Tibetan Plateau and in Central Asia to diversify their crops. A second event at 2000 cal. BP leads farmers in central China to also diversify their cropping systems and to develop systems that allowed transport of grains from southern to northern China. In other areas where crop returns fared even worse, humans reduced their risk by increasing investment in nomadic pastoralism and developing long-distance networks of trade. By translating changes in climatic variables into factors that mattered to ancient farmers, we situate the adaptive strategies they developed to deal with variance in crop returns in the context of environmental and climatic changes.
d’Alpoim Guedes, Jade A., Stefani A. Crabtree, R. Kyle Bocinsky, and Timothy A. Kohler. 2016 Twenty-first Century Approaches to Ancient Problems: Climate and Society. Proceedings of the National Academy of Sciences. published ahead of print December 12, 2016, DOI: 10.1073/pnas.1616188113 (click to download)
By documenting how humans adapted to changes in their environment that are often much greater than those experienced in the instrumental record, archaeology provides our only deep-time laboratory for highlighting the circumstances under which humans managed or failed to find to adaptive solutions to changing climate, not just over a few generations but over the longue durée. Patterning between climate-mediated environmental change and change in human societies has, however, been murky because of low spatial and temporal resolution in available datasets, and because of failure to model the effects of climate change on local resources important to human societies. In this paper we review recent advances in computational modeling that, in conjunction with improving data, address these limitations. These advances include network analysis, niche and species distribution modeling, and agent-based modeling. These studies demonstrate the utility of deep-time modeling for calibrating our understanding of how climate is influencing societies today and may in the future.
Bocinsky, R. Kyle. 2015. PaleoCAR: Paleoclimate Reconstruction from Tree Rings using Correlation Adjusted correlation. R package version 2.1. https://github.com/bocinsky/PaleoCAR/archive/2.1.tar.gz. This is the engine that created the paleoclimatic reconstructions featured in the prototype.
Bocinsky, R. Kyle. 2016. FedData: Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources. R package version 2.0.4. http://CRAN.R-project.org/package=FedData
Bocinsky, R. Kyle, and Timothy A. Kohler. 2014. A 2,000-year reconstruction of the rain-fed maize agricultural niche in the US Southwest. Nature Communications 5:5618. DOI: 10.1038/ncomms6618.
First application of the PaleoCAR method, applied to the two VEPII areas. The maize dry-farming niche was reconstructed for these two areas by thresholding the net water-year precipitation reconstruction at the 30-cm isohyet, and the growing-season growing degree days reconstruction at 1800 (measured in Fahrenheit GDDs), and then overlaying; areas satisfying both requirements are considered to be in the niche. Relative to their own long-terms means, the northern area (a large portion of the central Mesa Verde region) was below its mean niche size, and the southern area (a large portion of the northern Rio Grande area) was above its mean niche size, for the entire period from AD 1150 to 1250, during which the exodus from the northern to the southern area began.
Bocinsky, R. Kyle, Johnathan Rush, Keith W. Kintigh, and Timothy A. Kohler. 2016. Exploration and exploitation in the macrohistory of the prehispanic Pueblo Southwest. Science Advances 2, e1501532. DOI: 10.1126/sciadv.1501532.
By playing off the maize dry-farming niche reconstruction, now estimated for the entire Four Corners states through high-performance computing– against the record of almost 30,000 tree-ring dates, we address the classic problem of what causes the patterns recognized in the late 1920s in the Pecos Classification. We find that the second portion of each of the periods from Basketmaker III – Pueblo III contains a peak in construction marked by peaks in tree-ring dates, and particularly by peaks in cutting dates. These periods of “exploitation” are also marked by regional congruence in architectural and ceramic styles that define the canon by which each of these periods is recognized. Each of these periods comes to a close as the maize dry-farming niche contracts. The size of the contraction is positively correlated with the amount of change going into the next period. Each of these peaks in construction is followed by a period of “exploration” marked by disaggregation, fewer tree-ring cutting dates, and less regional structure to the settlement pattern. Eventually new niches (that are jointly ecological, social, and organizational) are developed, and the cycle begins anew. The use of tree rings for both niche reconstruction and dating allow us for the first time to see what the maize-growing conditions are in those portions of the Southwest that are actually inhabited in any year from AD 500-1400, the temporal limits of the study.
Grimstead, Deanna N., Matthew C. Paile, and R. Kyle Bocinsky. 2017 Refining Potential Source Regions via Combined Maize Niche Modeling and Isotopes: a Case Study from Chaco Canyon, NM, USA. Journal of Archaeological Method and Theory, Published online 16 December 2017. doi: 10.1007/s10816-017-9359-6
Abstract The application of geochemical sourcing methods to archaeological questions
continues to grow, as does the need for innovation in applying these methods. The process of sourcing materials is to rule out potential areas in favor of the most likely origin. It will foreseeably remain true that additional data could reveal other potential sources for an artifact. However, the use of multiple methods to further refine potential sources should not be neglected. In this paper, we use maize niche modeling in tandem with isotopic data to refine possible source regions of archaeological deer from Chaco Canyon, NM, USA (ca. AD 800–1250). Previous research on this prehistoric community demonstrated an extensive non-local procurement system where small mammals were garden-hunted in plots lying > 40 km from the canyon and the procurement of deer from upper elevations at > 90 km. The upper elevation procurement of deer will be tested by adding carbon isotopes and maize niche modeling to previously published strontium and oxygen isotopic data. As browsers with an affinity for maize, deer harvested in low to mid elevations within the maize farming niche should have carbon isotope ratios reflecting C4 plant consumption. Growing degree days in this region place the most salient limits on the elevation of maize production and define the region corresponding to a maize-free diet. Analyses of archaeofaunal deer from Pueblo Bonito indicate that hunting occurred at a higher elevation than the maize farming niche. These results demonstrate the utility of combining geochemical sourcing methods with paleoenvironmental modeling.
Kintigh, Keith W., Katherine A. Spielmann, Adam Brin, K. Selçuk Candan, Tiffany Clark, and Matthew Peeples. 2018. Data Integration in the Service of Synthetic Research. Advances in Archaeological Practice. 6(1): 30-41. DOI:10.1017/aap.2017.33
Addressing archaeology’s most compelling substantive challenges requires synthetic research that exploits the large and rapidly expanding corpus of systematically collected archaeological data. That, in turn, requires a means of combining datasets that employ different systematics in their recording while at the same time preserving the semantics of the data. To that end, we have developed a general procedure that we call query-driven, on-the-fly data integration that is deployed within the Digital Archaeological Record digital repository. The integration procedure employs ontologies that are mapped to the original datasets. Integration of the ontology-based dataset representations is done at the time the query is executed, based on the specific content of the query. In this way, the original data are preserved, and data are aggregated only to the extent necessary to obtain semantic comparability. Our presentation draws examples from the largest application to date: an effort by a research community of Southwest US faunal analysts. Using 24 ontologies developed to cover a broad range of observed faunal variables, we integrate faunal data from 33 sites across the late prehistoric northern Southwest, including about 300,000 individually recorded faunal specimens.
Kohler , Timothy A., and R. Kyle Bocinsky. 2017. Crises as Opportunities for Culture Change. In Crisis to Collapse: The Archaeology of Social Breakdown, edited by Tim Cunningham and Jan Driessen, pp. 263-273. AEGIS 11. Presses Universitaires de Louvain, Louvain-la-Neuve, Belgium. http://pul.uclouvain.be/book/?gcoi=29303100293170.
Archaeology depends on, and generates, proxy paleoclimatic and paleoenvironmental data. We review various initiatives, most quite recent, by which archaeologists seek to make these data more readily discoverable and useful, to facilitate the cumulation of research.
Kohler, Timothy A., P.I. Buckland, K.W. Kintigh, R.K. Bocinsky, A. Brin, A. Gillreath-Brown, B. Ludäscher, T.M. McPhillips, R. Opitz, and J. Terstriep. 2018. Paleodata for and from archaeology. PAGES Magazine 26(2):68-69. DOI: 10.22498/pages.26.2.68 (downloadable from the link).
Marwick, Ben E. (editor) How to do ArchaeologIcal Science using R. Draft EBook. https://benmarwick.github.io/How-To-Do-Archaeological-Science-Using-R/ (Contribution by R. Kyle Bocinsky)
This website is a early draft of an edited volume of contributions to the ‘How To Do Archaeological Science Using R’ forum of the 2017 Society of American Archaeology annual meeting in Vancouver, BC. The forum was organised by Ben Marwick, who is the editor of this collection.
McPhillips,Timothy, Tianhong Song,Tyler Kolisnik, Steve Aulenbach, Khalid Belhajjame, Kyle Bocinsky, Yang Cao, James Cheney, Fernando Chirigati, Saumen Dey, Juliana Freire, Christopher Jones, James Hanken, Keith W. Kintigh, Timothy A. Kohler, David Koop, James A. Macklin, Paolo Missier, Mark Schildhauer, Christopher Schwalm, Yaxing Wei, Mark Bieda, Bertram Ludäscher. 2015. YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts. International Journal of Digital Curation 10(1): 298–313. DOI: 10.2218/ijdc.v10i1.370
Scientific workflow management systems offer features for composing complex computational pipelines from modular building blocks, executing the resulting automated workflows, and recording the provenance of data products resulting from workflow runs. Despite the advantages such features provide, many automated workflows continue to be implemented and executed outside of scientific workflow systems due to the convenience and familiarity of scripting languages (such as Perl, Python, R, and MATLAB), and to the high productivity many scientists experience when using these languages. YesWorkflow is a set of software tools that aim to provide such users of scripting languages with many of the benefits of scientific workflow systems. YesWorkflow requires neither the use of a workflow engine nor the overhead of adapting code to run effectively in such a system. Instead, YesWorkflow enables scientists to annotate existing scripts with special comments that reveal the computational modules and dataflows otherwise implicit in these scripts. YesWorkflow tools extract and analyze these comments, represent the scripts in terms of entities based on the typical scientific workflow model, and provide graphical renderings of this workflow-like view of the scripts. Future version of YesWorkflow will also allow the prospective provenance of the data products of these scripts to be queried in ways similar to those available to users of scientific workflow systems.