WK 21 - An Introduction to Estimating Ecological Change with the US Forest Service Forest Inventory and Analysis (FIA) Database

Session Description: Forests globally are undergoing rapid transformations as a result of changing climates, disturbance regimes, and human pressures. Building a predictive understanding of these transformations is critical for maintaining the many important functions forests provide; however, this requires information of a scope and scale surpassing most ecological datasets. National forest inventories, like the United States Forest Service Forest Inventory and Analysis (FIA) program, can provide these data resources. The FIA program is an incredibly rich source of ecological information, comprising more than 355,000 permanent and standardized forest plots containing upwards of 19 million individually tracked trees. While the FIA program was designed for determining the status and trends of forest resources in the US, it is now increasingly being used to answer a wider variety of questions related to global change. Many of these new research questions and applications require balancing the scale and scope of FIA data collection with the specific research questions being addressed. Despite the expertise and creativity necessary to do this, research interest in the database is high and continues to grow.

This session will provide an introduction to the FIA database, with a focus on tools and resources available to help new users avoid common pitfalls. The session will begin with an overview of the FIA sampling design and data collection protocol, both of which form a critically important foundation for using the database correctly. The workshop will then introduce resources available to help new users access, navigate, and subset the database. We will use these resources, including pre-written R code, to generate basic change estimates using the FIA database. Finally, the workshop will close with a “do’s and don’t’s” session with US Forest Service analysts – this final component will help participants establish good data practices, as well as ask questions of scientists who are closely familiar with the database.