Predictive modelling of fossil-bearing locality distributions in the Elliot Formation (Upper Triassic–Lower Jurassic), South Africa, using a combined multivariate and spatial statistical analyses of present-day environmental data

摘要

AbstractTraditional methods of palaeontogical fieldwork often involve costly and inefficient searching for suitable fossil-bearing sites. The advent of digital spatial data offers an opportunity to improve fieldwork search efficiency. In this paper, we develop a model that identifies potential fossil localities at the reconnaissance stage of prospecting using a criterion of present-day environmental suitability for fossil discovery. This model employs techniques from analytical biology, Geographical Information Systems (GIS), and remote sensing. The model is designed to be flexible with no limit on the number of variables and can use a variety of data types (both continuous and categorical) by means of a principle coordinate analysis (PCO). We tested the model on outcrops of the Elliot Formation (Upper Triassic–Lower Jurassic) in the Free State, South Africa. Our model correctly predicted 95% of the known occurrences and reduced the potential prospective area from approximately 2300km2of mapped total outcrop to 597km2of prospectable outcrop. Initial field testing in 2015 identified two new sites based on model output.Highlights?Predictive model for fossil bearing localities is establis