1. Modeling in the presence of uncertainty: Is there another way to do it? Jennifer Hoeting Colorado State University

2. New conference title: “The movers and shakers of ecological modeling” A big thank you to the workshop organizers: Noel, Jim, Jay, Kate, Chris; and the faculty and staff of MBI … even if this conference did force a University of Michigan alumna to attend a function at archrival Ohio State – Go Blue!

3. Two Themes for Discussion 1.Uncertainty 2.Graduate Education

4. Uncertainty Are we doing enough (yet) to convey uncertainty in analyses of ecological problems? Does too much focus on uncertainty distract from the main point of our analyses (inference)?

5. Uncertainty: Is there a best method for model selection/specification? Bayesian (e.g., Gelfand): use MANY parameters and let the data speak for themselves Semi-parametric (e.g., Breidt): combine model-based and design-based inference Likelihood (e.g., Burnham): AIC is taking the world of ecology by storm. Is this “new religion” the right direction for ecology?

6. Uncertainty: Is there a best method for model selection/specification? Does our choice of model or analysis depend on whether the data are from a probability-based sample (Breidt) or an observational study (Gelfand and Breidt)? Are covariates really “known covariates”?

7. Graduate Education Are we equipping our graduate students with the right tools to become the next Cressie, Gelfand, Clark, Hastings, or insert your name here? How much ecology do I need to know?

8. Graduate Education If the focus is to train statisticians who will impact ecology, what is the right blend of traditional and modern statistical methods in graduate study? Multi-disciplinary is all the rage in academia. Does an interdisciplinary degree water down a statistics or ecology degree?