Bayesian Species Distribution Modelling of Domesticated Plants
Channel: Recording Archaeology
Date: 27/11/2020

Species Distribution Modelling (SDM) has been a rapidly developing methodology within fields such as ecology and paleobiology, but its potential within archaeology has not been fully ex-plored. The few existing studies often rely on the Maximum Entropy algorithm (MaxEnt), which is a machine learning technique and is the most widely used approach. However, recent research suggests that the hierarchical Bayesian framework, which allows to model both ecological processes and observation processes, may be more suitable to limited and spatially uneven data. As the Bayesian approach is receiving an increasing interest within archaeology, this paper will present its application in the modelling of the paleodistribution of domesticated buckwheat, constructed as part of the ‘Crops, Pollinators and People’ project. With this method, the project aims to identify factors that restricted the spread of buckwheat and map its potential routes of dispersal. With reference to this study, the paper will reflect on the applicability and limitations of SDM in research related to the spread of agriculture. In particular, it will discuss the quality of archaeobotanical data used as species occurrence records and environmental reconstructions, as well as the differences between the results obtained with MaxEnt and Bayesian methods and the  impact  of  the  modelling  approach  on  their  interpretation.  In  conclusion,  I  will  consider  the methodological implications of the study in the context of increasing interest in the further applications of SDMs and Bayesian approaches in archaeology.


Marta Krzyzanska, Enrico Ryunosuke Crema