Abstract
1. Analog forecasting has been successful at producing robust forecasts for a
variety of ecological and physical processes. Analog forecasting is a
mechanism-free nonlinear method that forecasts a system forward in time by
examining how past states deemed similar to the current state moved forward.
Previous work on analog forecasting has typically been presented in an
empirical or heuristic context, as opposed to a formal statistical context. 2.
The model presented here extends the model-based analog method of McDermott and
Wikle (2016) by placing analog forecasting within a fully hierarchical
statistical frame- work. In particular, a Bayesian hierarchical
spatial-temporal Poisson analog forecasting model is formulated. 3. In
comparison to a Poisson Bayesian hierarchical model with a latent dynamical
spatio- temporal process, the hierarchical analog model consistently produced
more accurate forecasts. By using a Bayesian approach, the hierarchical analog
model is able to quantify rigorously the uncertainty associated with forecasts.
4. Forecasting waterfowl settling patterns in the northwestern United States
and Canada is conducted by applying the hierarchical analog model to a breeding
population survey dataset. Sea Surface Temperature (SST) in the Pacific ocean
is used to help identify potential analogs for the waterfowl settling patterns.