Climate change, not human population growth, correlates with Late Quaternary megafauna declines in North America
This repo contains the R code used for the study presented in the following paper:
Climate change, not human population growth, correlates with Late Quaternary megafauna declines in North America. Please see the paper and associated SI for additional information (including the data required for replication efforts, a much clearer presentation of the analyses described in the paper, up-to-date scripts and a fully documented R markdown file for replication)
The disappearance of many North American megafauna at the end of the Pleistocene is a contentious topic. While the proposed causes for megafaunal extinction are varied, most researchers fall into three broad camps emphasizing human overhunting, climate change, or some combination of the two. Understanding the cause of megafaunal extinctions requires the analysis of through-time relationships between climate change and megafauna and human population dynamics. To do so, many researchers have used summed probability density functions (SPDFs) as a proxy for through-time fluctuations in human and megafauna population sizes. SPDFs, however, conflate process variation with the chronological uncertainty inherent in radiocarbon dates. Recently, a new Bayesian regression technique was developed that overcomes this problem—Radiocarbon-dated Event-Count (REC) Modelling. Here we employ REC models to test whether declines in North American megafauna species could be best explained by climate changes, increases in human population densities, or both, using the largest available database of megafauna and human radiocarbon dates. Our results suggest that there is currently no evidence for a persistent through-time relationship between human and megafauna population levels in North America. There is, however, evidence that decreases in global temperature correlated with megafauna population declines.
The R scripts contained in this repository are intended for replication efforts and to improve the transparency of my research. They are, of course, provided without warranty or technical support. That said, questions about the code can be directed to me, Chris Carleton, at ccarleton@protonmail.com.
This analysis described in the associated manuscript was performed in R. Thus, you may need to download the latest version of R in order to make use of the scripts described below.
This project made use of a Bayesian Analysis package called Nimble. See the Nimble website for documentation and a tutorial. Then, refer to the R scripts in this repo.
For a more detailed replication demonstration, see the Wiki associated with this repo and---importantly---the supplementary information provided alongside the paper.