Out with the new, in with the old: recent advances in palaeoecological modelling with open data
Paper presented at the Association for Environmental Archaeology Spring Meeting 2021: Open Science Practices in Environmental Archaeology, Oxford, 2021.
Computational models of past environments and ecological dynamics on a regional scale can be a useful complement to conventional site-based environmental archaeology.
Modelling past ecosystems has become significantly easier in recent years with the development of several open datasets,
including high resolution global climate and environmental data (e.g.
SoilGrids), downscaled palaeoclimate reconstructions from global circulation models (e.g.
PaleoClim), and biodiversity data (e.g.
At the same time, better computational tools have made complex modelling methods more accessible and reproducible.
The statistical programming language
R has become the core of both computational archaeologists’ and ecologists’ toolkits, bringing with it an extensive collection of packages for working with open environmental data (e.g. the
rOpenSci project), archaeological data (e.g.
rcarbon), and geostatistical models (e.g.
In this paper, I review these advances and their potential for palaeocological modelling in an archaeological context, using as a case study environmental niche modelling applied to reconstructing the palaeodistribution of human-exploited flora and fauna in Epipalaeolithic Southwest Asia. The results highlight the value of open environmental data in understanding the regional ecosystems in which past humans were embedded, but there remain considerable methodological challenges in integrating such ‘top-down’ models with ‘bottom-up’ insights from environmental archaeology. Archaeological data is now the weak link: fragmentation, lack of standardisation, and the need for time-consuming digitisation of ‘legacy’ datasets hampers the direct integration of archaeological and ecological understanding. I therefore argue that continuing to improve the accessibility of environmental archaeology data is key to building better models of past ecosystems.