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Landscape rules predict optimal superhighways for the first peopling of Sahul

Nature Human Behaviour volume 5, pages 1303–1313 (2021)Cite this article

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Abstract

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

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Fig. 1: Data layers used to create optimal pathways.
Fig. 2: Visual-prominence aggregate viewshed for the entire Sahul landmass.
Fig. 3: Model-averaged pathway probabilities.

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Data availability

All thresholded binary masks created from all FETE runs, the model-averaged composite grid, lakes and archaeological sites are made publicly available in standard geospatial data formats as part of our Supplementary Data. Due to large file size, we provide the two digital elevation models and aggregate viewshed in standard geospatial data formats via GitHub: github.com/dawhite/sfa.

Code availability

Due to how the development of FETE was funded, it is not currently possible to make the source code available to the public. As an alternative to public release of the code, the full methodology of the baseline version of FETE is described in White and Barber21 and we describe modifications made to support our study here. Researchers who are interested in replicating the functionality of FETE can do so by using an open-source or commercial GIS software package in combination with basic scripting in a high-level programming language such as Python and following the methodology described herein. We provide all code used for analyses via GitHub: github.com/dawhite/sfa.

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Acknowledgements

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the US Department of Energy or the United States Government. This research was done by the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage (CE170100015) and the ASU-SFI Center for Biosocial Complex Systems. M.I.B. is the recipient of an Australian Research Council Laureate Fellowship (FL140100044). We thank the Santa Fe Institute for support in beginning this research. For assistance in developing the basis of the paper, we thank K. Norman, F. Petchey, M. Price, S. Slater, C. McGuire and R. Wood. We thank R. Roberts for comments on the paper. Approved for public release: SAND2021-1499 J.

Author information

Author notes

  1. These authors contributed equally: Stefani A. Crabtree and Devin A. White, Sean Ulm and Michael I. Bird, Corey J. A. Bradshaw and Frédérik Saltré.

Authors and Affiliations

  1. Department of Environment and Society, Utah State University, Logan, UT, USA

    Stefani A. Crabtree

  2. The Santa Fe Institute, Santa Fe, NM, USA

    Stefani A. Crabtree

  3. ARC Centre of Excellence for Australian Biodiversity and Heritage, James Cook University, Cairns, Queensland, Australia

    Stefani A. Crabtree, Michael I. Bird & Sean Ulm

  4. Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI), Paris, France

    Stefani A. Crabtree

  5. Autonomous Sensing and Perception, Sandia National Laboratories, Albuquerque, NM, USA

    Devin A. White

  6. Department of Anthropology, University of Tennessee, Knoxville, Knoxville, TN, USA

    Devin A. White

  7. ARC Centre of Excellence for Australian Biodiversity and Heritage, Flinders University, Adelaide, South Australia, Australia

    Corey J. A. Bradshaw & Frédérik Saltré

  8. Global Ecology Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia

    Corey J. A. Bradshaw & Frédérik Saltré

  9. Climate Change Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia

    Alan N. Williams

  10. ARC Centre of Excellence for Australian Biodiversity and Heritage, University of New South Wales, Sydney, Australia

    Alan N. Williams

  11. EMM Consulting Pty Ltd, St Leonards, New South Wales, Australia

    Alan N. Williams

  12. College of Science and Engineering, James Cook University, Cairns, Queensland, Australia

    Robin J. Beaman & Michael I. Bird

  13. College of Arts, Society and Education, James Cook University, Cairns, Queensland, Australia

    Sean Ulm

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  1. Stefani A. Crabtree
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Contributions

S.A.C., S.U., M.I.B. and C.J.A.B. obtained funding from the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage and the ASU-SFI Center for Biosocial Complex Systems. D.A.W. and S.A.C. developed the FETE models. D.A.W., S.A.C. and F.S. did statistical evaluations. R.J.B. compiled seamless terrestrial and bathymetric digital elevation model data. S.A.C., D.A.W., S.U., A.N.W, C.J.A.B., F.S. and M.I.B. wrote the main text with specialist contributions from other authors. All authors provided comments and revisions.

Corresponding author

Correspondence to Stefani A. Crabtree.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Claudine Gravel-Miguel, Emma Slayton, Colin Wren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Data layers used to create all-of-Sahul dataset.

The Sahul digital elevation model is an amalgamation of existing regional- and global-scale digital elevation models as shown here.

Extended Data Fig. 2 Bathymetry of Sahul at >50,000 years.

Base layers Natural Earth 1:10 M Land Polygons and SRTM-derived 1 Second Digital Elevation Models Version 1.0103. All data are available under Creative Commons Attribution 4.0 International Licence (http://creativecommons.org/licenses/).

Extended Data Fig. 3 Visual prominence of Sahul at >50,000 years.

Base layers Natural Earth 1:10 M Land Polygons and SRTM-derived 1 Second Digital Elevation Models Version 1.0103. All data are available under Creative Commons Attribution 4.0 International Licence (http://creativecommons.org/licenses/).

Extended Data Fig. 4 Bathymetry of Sahul at >50,000 years, plus Strahler Stream Order 9 streams extended to the coastline.

Base layers Natural Earth 1:10 M Land Polygons and SRTM-derived 1 Second Digital Elevation Models Version 1.0103. All data are available under Creative Commons Attribution 4.0 International Licence (http://creativecommons.org/licenses/).

Extended Data Fig. 5 Superhighways of Sahul at >50,000 years with no archaeological sites.

Superhighways of Sahul at >50,000 years with no archaeological sites or labels included for figure reproduction purposes.

Extended Data Fig. 6 Superhighways of Sahul at >50,000 years with archaeological sites of >35,000 years, no labels.

Superhighways of Sahul at >50,000 years with archaeological sites of >35,000 years, no labels included for figure reproduction purposes.

Extended Data Fig. 7 Superhighways of Sahul at >50,000 years with archaeological sites of >35,000 years, with labels.

Superhighways of Sahul at >50,000 years with archaeological sites of >35,000 years, labels included for clarity.

Extended Data Fig. 8 Named archaeological sites from reference archaeological dataset.

Taken with Supplementary Table 8, readers can assess the age of sites corresponding to paths across Sahul.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5 and Supplementary Tables 1–9.

Reporting Summary

Supplementary Table

Supplemental Tables 1–9 in a combined workbook with multiple tabs.

Supplementary Data

This file contains five folders or code and data: (1) Supplemental Network Raw Rasters. Raw output for each of our scenarios in the form of binary raster masks for each model at all cutoffs. These data are the foundation input for determining distances between sites and networks. (2) Shapefile containing the sites presented in Supplementary Table 1. (3) Raw raster version of the path network map presented in Fig. 3. (4) Shapefiles containing best-available-approximation polygons for ancient lakes in the study region. (5) Binary raster mask for the landmass of Sahul used in this analysis.

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Crabtree, S.A., White, D.A., Bradshaw, C.J.A. et al. Landscape rules predict optimal superhighways for the first peopling of Sahul. Nat Hum Behav 5, 1303–1313 (2021). https://doi.org/10.1038/s41562-021-01106-8

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