Peopling of Sahul

R code for reproducing age-structured models of ancient humans entering Sahul.

Prof Corey J. A. Bradshaw
Global Ecology, Flinders University, Adelaide, Australia
September 2021
e-mail

The repository's code and associated data are to reproduce the models presented in the following companion papers:

• Bradshaw, CJA, S Ulm, AN Williams, MI Bird, RG Roberts, Z Jacobs, F Laviano, LS Weyrich, T Friedrich, K Norman, F Saltré. 2019. Minimum founding populations for the first peopling of Sahul. Nature Ecology and Evolution 3: 1057-1063. doi:10.1038/s41559-019-0902-6

• Bird, MI, SA Condie, S O’Connor, D O’Grady, C Reepmeyer, S Ulm, M Zega, F Saltré, CJA Bradshaw. 2019. Early human settlement of Sahul was not an accident. Scientific Reports 9: 8220. doi:10.1038/s41598-019-42946-9

The first R code file humanpopmodelgithub.R is a stochastic projection of the human population entering Sahul required to estimate minimum viable population size. It requires the following R libraries:

• boot
• tcltk
• sp
• rgdal
• raster

The code also requires the source code: matrixOperators.r (provided in this repository), as well as the following data files:

• 'world2013lifetable.csv' (modern human demographic data from Bradshaw & Brook. 2014. Human population reduction is not a quick fix for environmental problems. Proceedings of the National Academy of Sciences of the USA 111: 16610–16615. doi:10.1073/pnas.1410465111)
• 'ClimateSahul_Npp.csv' (hindcasted net primary production values for northern Sahul produced by the LOVECLIM global circulation model)

The second R code file human arrival population model_github.R is a variant of the above, only this time applied to the island-hopping scenario presented in the Bird et al. analysis.

This code requires the following R libraries:

• boot
• tcltk
• plotly
• sp
• rgdal
• raster

and the same data files as above ('world2013lifetable.csv' & 'ClimateSahul_Npp.csv').

The repository also includes a global sensitivity analysis ancienthumanfound_gsa_sim_func_v2.R derived from the paper: Prowse, TAA, CJA Bradshaw, et al. 2016. An efficient protocol for the global sensitivity analysis of stochastic ecological models. Ecosphere 7: e01238. doi:10.1002/ecs2.1238. This requires the following R libraries:

• iterators
• snow
• doSNOW
• foreach
• lhs
• data.table

The sensitivity analysis also relies on the source code: matrixOperators.r (provided in this repository), as well as the following data files:

• 'ClimateSahul_Npp.csv' (hindcasted net primary production values for northern Sahul produced by the LOVECLIM global circulation model)
• 'world2013lifetable.csv' (modern human demographic data from Bradshaw & Brook. 2014. Human population reduction is not a quick fix for environmental problems. Proceedings of the National Academy of Sciences of the USA 111: 16610–16615. doi:10.1073/pnas.1410465111)

All code ran on the Flinders University Deepthought High-Performance Computing facility: Flinders University (2021). DeepThought (HPC). doi:10.25957/FLINDERS.HPC.DEEPTHOUGHT