Using Forest Inventory and Analysis Data to Track Rust

Data from the most recent survey in each State were used to estimate the current status of rust infection in each Southern State where rust occurs. In addition, data from the next-to-last survey was compared to the most recent in order to assess short-term changes in rust infection. Finally, in four States (Mississippi, North Carolina, South Carolina, and Virginia) where three surveys that contained rust data were available, we made a longer-term assessment of change in rust status.

The analysis was restricted to FIA plots with slash or loblolly pine forest types. For these plots a subset of the data was created from each survey that included State, forest type (slash or loblolly), ownership, number of infected pines (slash or loblolly) per acre, number of healthy pines (slash or loblolly) per acre, stand age, stand origin (planted or natural), and the appropriate plot expansion factors (for calculating State-level estimates of acres). State-level estimates of the number of acres with >10-, >30-, and >50-percent rust incidence were calculated by multiplying the number of plots with those levels by their associated plot expansion factors and summing the results. Estimates were made for the slash and loblolly pine types, planted and natural stand origins, and several ownership categories for each State. Statewide estimates of incidence (at the >10-percent level) are based on hundreds of plots per State and are, therefore, very reliable. Further breakdowns by host type, stand origin, or higher incidence levels result in less reliability because fewer plots occur in these classifications. Thus, the latter estimates should be viewed with more caution.