Strontium isotope ratios ( 87 Sr/ 86 Sr) of archaeological samples (teeth and bones) can be used to track mobility and migration across geologically distinct landscapes. However, traditional interpolation algorithms and classification approaches used to generate Sr isoscapes are often limited in predicting multiscale 87 Sr/ 86 Sr patterning. Here we investigate the suitability of plant samples and soil leachates from the IRHUM database (www.irhumdatabase.com) to create a bioavailable 87 Sr/ 86 Sr map using a novel geostatistical framework. First, we generated an 87 Sr/ 86 Sr map by classifying 87 Sr/ 86 Sr values into five geologically-representative isotope groups using cluster analysis. The isotope groups were then used as a covariate in kriging to integrate prior geological knowledge of Sr cycling with the information contained in the bioavailable dataset and enhance 87 Sr/ 86 Sr predictions. Our approach couples the strengths of classification and geostatistical methods to generate more accurate 87 Sr/ 86 Sr predictions (Root Mean Squared Error = 0.0029) with an estimate of spatial uncertainty based on lithology and sample density. This bioavailable Sr isoscape is applicable for provenance studies in France, and the method is transferable to other areas with high sampling density. While our method is a step forward in generating accurate 87 Sr/ 86 Sr isoscapes, the remaining uncertainty also demonstrates that fine-modelling of 87 Sr/ 86 Sr variability is challenging and requires more than geological maps for accurately predicting 87 Sr/ 86 Sr variations across the landscape. Future efforts should focus on increasing sampling density and developing predictive models to further quantify and predict the processes that lead to 87 Sr/ 86 Sr variability.