Researchers who study ecological systems strive to understand the factors that influence extremely complex systems, in which tens if not hundreds of environmental factors can affect the distribution and abundance of a species. Challenges in managing a species across its entire geographic range are especially great, as scaling up insights from small local studies to management over an entire continent may be impossible. Further, initial management decisions may need to be made under very short time deadlines, and in the absence of much prior knowledge about a species. Often, the necessary data to inform timely management decisions for little-known species already exist; however, what is limiting is ready access to the data and especially to the analytical tools needed to explore these data. This project joins the strengths of data mining and machine learning tools with statistical methods, to create a suite of powerful new predictive and inferential tools in a new analytical framework for data mining and machine learning that will permit extracting more relevant information from the available data and by incorporating prior information into a hybrid hierarchical/data mining model. The result will be new data-mining techniques that allow statistical inferences about large numbers of environmental variables and their potential interactions. This will greatly enhance the ability to model the landscape-level response of bird populations to multiple risk factors and to develop prescriptions for reversing population declines through land management. This project will expose new data resources and advances in computational analysis, data visualizations, and manipulations to vast new audiences: from biologists, conservation agencies, and land-use planners to school classrooms and tens of thousands of citizens who participate in environmental monitoring, including the millions of people across the country who watch birds. Additionally, the project trains new researchers in the union of powerful statistical techniques with machine learning and data mining
