d’Alpoim Guedesa, 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.  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.

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.

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.

Marwick, Ben E. (editor) How to do Archaeologcial Science using R.  Draft EBook. (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.