University of Southampton OCS (beta), CAA 2012

Font Size: 
Predictive Modelling in Rock Art Research Without Absence Data
Michelle Lynn Wienhold

Last modified: 2011-12-17


Predictive modelling without absence data has become a valuable and highly successful method of analysis in conservation ecology in recent years.  This is because absence data of species is often hard to accurately confirm due to potential sample bias. Ecologists have therefore created a GIS-based modelling tool (Biomapper, an open-source software) using multivariate statistics to link species to their most suitable habitats.  In this study, the same tool was used to test presence-only modelling on archaeological datasets, and more specifically, on Chumash rock art data from Los Padres National Forest in South-Central California.  Rock art data was chosen because it is an immovable artefact that is fixed in the landscape and previous studies have shown that context can play an important role in its placement.  Los Padres National Forest is located on over 400,000 hectares of land, over very rugged topography and very little data is currently available from previous surveys.  A predictive model is beneficial in guiding rock art surveys across this diverse landscape especially when applied at multiple scales of analysis.  Furthermore, this type of analysis across the cultural and geographic scales, reveals the different components between the landscape and the geographies of the Indigenous populations.  Finally, this study aims to find ways in which presence-only modelling can be applied to other archaeological datasets that lack strong absence data and are linked to locational context.


Presence-only modelling; multivariate statistics; Biomapper; rock art