Ecologists are interested in understanding changes in tree species abundances and spatial distributions over thousands of years since the last glacial maximum. To estimate forest composition and investigate how much information is available from fossil pollen deposited in lake sediments, we build a Bayesian spatio-temporal hierarchical model that predicts forest composition in southern New England, USA, based on fossilized pollen. The critical relationships between abundances of taxa in the pollen record and abundances in actual vegetation are estimated using modern data and data from colonial records, for which both pollen and direct vegetation data are available. For these time periods, the Bayesian model relates pollen and vegetation data to a latent multivariate spatial process representing forest composition, which allows estimation of several key parameters. For time periods in the past, we use only pollen data and the estimated model parameters to make predictions and assess uncertainty about the latent spatio-temporal process over the last 2000 years. A new graphical assessment of feature significance allows us to infer which spatial patterns are reliably estimated.
Group for Research in Decision Analysis