Ecosystems are inherently complex, and despite efforts to identify and model causal chains linking ecosystem disturbances with ecosystem response, there are inevitable discrepancies between observed and predicted conditions in the natural environment. Uncertainty, variability, and change all contribute to these differences, yet they are often ignored in predicting environmental problems. Statistical modeling techniques represent a general classification of tools that can help address discrepancies between predictions and observations, and Bayesian statistics in particular has recently been demonstrated to be a novel and effective tool for forecasting environmental pollutant problems because of its unique approach to quantifying uncertainty and variability.