Christopher Paciorek(Harvard School of Public Health)

Mapping Ancient Forests: Bayesian Inference for Forest Composition Using the Fossil Pollen Proxy Record

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 eposited 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 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 helps to infer which spatial patterns are reliably estimated. The modeling involves a complex hierarchical model that integrates disparate data sources. I will discuss a variety of issues arising in such models and the practical strategies we used to address them. I will also emphasize the importance of understanding which aspects of the data inform which aspects of the model.