Unlocking the Forest Inventory and Analysis Database: Applications to Nation-Wide Forest Health Monitoring

Abstract

Forest Inventory and Analysis (FIA) is a US Department of Agriculture Forest Service program that aims to monitor changes in forests across the US. FIA hosts one of the largest ecological datasets in the world, though its complexity limits access for many potential users. \texttt{rFIA} is an R package designed to simplify the estimation of forest attributes using data collected by the FIA Program. Specifically, \texttt{rFIA} improves access to the spatio-temporal estimation capacity of the FIA Database via space-time indexed summaries of forest variables within user-defined population boundaries. The package implements multiple design-based estimators, and has been validated against official estimates and sampling errors produced by the FIA Program. The package has been made open-source is freely available for download from the Comprehensive R Archive Network. In recent decades, forests of the western US have experienced unprecedented change in climate and forest disturbance regimes, and widespread shifts in forest composition, structure, and function are expected in response. However, large-scale, comprehensive assessments of tree population performance have yet to be conducted in the region. We develop an index of forest population performance based on repeated censuses of field plots, and apply this index to assess the status of the most abundant tree species in the western US. Our study provides empirical evidence to suggest tree species in the western US are exhibiting strong divergence in population performance, with over half (70\%) of species experiencing range-wide population decline. We found spatial variation in population performance across the ranges of all species, indicating range shifts are already underway. Our results further indicate that species decline can seldom be attributed to a single forest disturbance agent, highlighting the importance of considering multiple risks factors in broad-scale forest management.