Applying Bayesian statistics to organism-based environmental reconstruction

Reconstruction of environmental conditions from biological indicators, such as chironomids, can provide valuable insight into the variance of natural systems and the extent of current environmental problems. Various numerical techniques have been developed for this challenging task, based on differe nt models and assumptions, but ignoring some of the uncertainties inherent in the reconstruction. We study the use of Bayesian modeling and inference in organism-based palae oenvironmental reconstruction. We propose a Bayesian model, BUM, and compar e it empirically with eight other methods, including the widely used weight ed averaging (WA) technique. The methods are evaluated on a surface-sedimen t chironomid training data set from 53 subarctic lakes in northern Fennosca ndia by comparing the prediction statistics of these data. The resulting ca libration models are also applied to fossil chironomid assemblages in order to evaluate the differences in Holocene temperature reconstructions. The e mpirical results indicate that BUM is competitive compared with the state-o f-the-art methods. We also describe a generic Bayesian framework for reconstruction, to demons trate Bayesian tools for reasoning with a variety of ecological response mo dels. Bayesian statistics support the "classical" approach to regression an d calibration whereas the state-of-the-art methods, including WA, are based on the conceptually more controversial "inverse" approach. Further, the us e of probability distributions rather than point estimates for species resp onses gives a principled method for handling uncertainty in response modeli ng.