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1. Analog forecasting has been successful at producing robust forecasts for avariety of ecological and physical processes. Analog forecasting is amechanism-free nonlinear method that forecasts a system forward in time byexamining how past states deemed similar to the current state moved forward.Previous work on analog forecasting has typically been presented in anempirical or heuristic context, as opposed to a formal statistical context. 2.The model presented here extends the model-based analog method of McDermott andWikle (2016) by placing analog forecasting within a fully hierarchicalstatistical frame- work. In particular, a Bayesian hierarchicalspatial-temporal Poisson analog forecasting model is formulated. 3. Incomparison to a Poisson Bayesian hierarchical model with a latent dynamicalspatio- temporal process, the hierarchical analog model consistently producedmore accurate forecasts. By using a Bayesian approach, the hierarchical analogmodel is able to quantify rigorously the uncertainty associated with forecasts.4. Forecasting waterfowl settling patterns in the northwestern United Statesand Canada is conducted by applying the hierarchical analog model to a breedingpopulation survey dataset. Sea Surface Temperature (SST) in the Pacific oceanis used to help identify potential analogs for the waterfowl settling patterns.

A Hierarchical Spatio-Temporal Analog Forecasting Model for Count Data