Informing disturbance patterns in eastern US forests using extreme events
Past and future disturbances will influence the structure, composition, and functional processes of forested ecosystems. Quantifying the rate and extents of extreme or “high impact” disturbance events in forest ecosystems is one knowledge gap in the efforts to monitor the effects of global change on forest ecosystem processes. In particular, a better understanding on the extent of various disturbances and their impact to forest carbon stocks can inform land use and land use change in forestry sectors for annual reporting of the National Greenhouse Gas Inventory. This project sought to quantify expected forest mortality rates resulting from common disturbance agents and investigate how mortality rates differ between forests of varying functional trait composition and diversity (e.g., a forest’s shade, flood, and drought tolerance and specific gravity). Common disturbance regimes included those related to weather, animal damage, fire, insects, and diseases. We used tree mortality information from the Forest Inventory and Analysis (FIA) Program and performed extreme value analyses to quantify expected return intervals for common disturbance regimes across eastern US forests.
Seven percent of all FIA plots in the eastern US displayed at least one forest disturbance from 2003 to 2012. Extreme value models indicated that for a fixed return period (e.g., 50 years), the greatest forest mortality was always predicted to occur from disturbances related to weather. For an example interpretation, results indicate annual mortality of eastern US forests from weather disturbance will exceed 32.8 m3/ha/yr on average once every 100 years. Forests with a larger mean flood tolerance and shade tolerance exhibited longer return levels, contributing to the potential that plant functional traits may play in determining disturbance patterns. In contrast, forests tolerant to drought displayed a lower return level relative to less drought tolerant ones. Through a combination of merging forest inventory information with mean and diversity estimates of functional traits, extreme event modeling techniques can inform how return levels are influenced by various forest disturbance patterns.