A two-week Summer Institute for graduate and post-graduate students will be conducted to introduce environmental scientists to modern statistical techniques that have emerged over the last decade. Ecological inference and predictions are currently limited by the many sources of variability that operate at a range of scales. There are, however, powerful tools available for analyzing processes characterized by these sources of uncertainty and variation, including hierarchical Bayes and Markov chain Monte Carlo simulation. However, the widespread use of these techniques in ecology has been hindered due to limited training options. The 2nd Summer Institute will follow the format of the highly successful 1st Institute held in June 2004. Participants will include advanced graduate student and postdoctoral associates selected by an open application process. Leading statisticians and ecologists will provide day-long presentations and hands-on training with computational techniques. Students will participate in small working groups, with the aim of producing a published volume and lecture notes. The 2nd Summer Institute will take place in 2006, retaining aspects of the format that worked well in the 1st Institute, and incorporating modifications that will improve its effectiveness.
By training a select group of quantitative environmental scientists who will in turn train others and use this training in their research, this 'summer school' will expedite the spread and use of modern statistical computation techniques. Adoption of these modern methods by ecologists and earth scientists will strengthen the field of ecological forecasting, enhance its reputation, and increase its usefulness to resource managers and policy makers. Many of the techniques that will be covered should directly enhance capacity to use network information available in distributed databases.
