Predictive Modelling for Archaeological Sites: Ashokan Edicts from the Indian Subcontinent
GENERAL ARTICLES Predictive modelling for archaeological sites: Ashokan edicts from the Indian subcontinent Thomas W. Gillespie*, Monica L. Smith, Scott Barron, Kanika Kalra and Corey Rovzar This article focuses on the stone inscriptions ascribed to Ashoka, the 3rd century BC ruler of the Mauryan dynasty in ancient India. The locations of 29 known inscriptions and 8 environmental predictors at 1 km pixel resolution were entered into a species distribution model, that reliably pre- dicted the distribution of known Ashokan edicts (AUC score 0.934). Geologic substrate (33%), population density in AD 200 (21%), and slope (13%) explained majority of the variance in the Ashokan edict locations. We have identified 121 possible locations in the Indian subcontinent that conform to the same criteria where yet undiscovered inscriptions may be found. Keywords: Archaeology, edict locations, environmental metrics, species distribution models, stone inscriptions. TWO factors have combined that could increase the use of provide confirmation that people found the locations suit- modelling in archaeology: the availability of global land- able and worthy of investment. scape geographic information system (GIS) datasets, and The predictability of archaeological site location is the need to identify and protect sites in areas jeopardized by based on a variety of criteria, not all of which are imme- development and other human impacts. An important po- diately apparent. For example, our earliest ancestors hun- tential contributor to this process is species distribution dreds of thousands of years ago utilized caves as shelters modelling, which has been increasingly used across a va- prior to the development of built architecture, but not all riety of fields, including biogeography, ecology, conserva- known caves were occupied within a given region due to tion biology, and climate change science to identify metrics additional factors of selection, such as the preference for that define and predict species and ecosystems ranges1–4.
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