Transfer function (TF) models are commonly used in palaeoecology for quantitative inference of environmental variables based on biological proxies. Although the existence of spatial structure is well established in ecology, existing TFs do not account for it. This suggests that model performance may be improved by accounting for spatial structure. Here we demonstrate this using basic and advanced methods - multiple linear regression (MLR), lasso regression, geographically weighted regression (GWR) and geographically weighted lasso (GWL) - using geographical distance and bioclimatic distance, respectively. We compared the performance of these models for reconstructing water table depth from testate amoeba communities, as commonly used in peatland palaeoecology. GWL and lasso models performed considerably better (23 %-30 % reduction in mean squared prediction error) than standard weighted average methods. We provide an R package for the two innovative spatial methods (GWR and GWL).