A New Severity Rating System for Evaluating and Predicting the Impacts of a Nonnative Invasive Forest Insect on Two Pacific Northwest Fir Species
Tuesday, 16 December 2014
Balsam woolly adelgid (BWA) is a nonnative invasive forest insect introduced from Europe to North America around 1900. The insect established and spread in the northeast, infesting and causing mortality of balsam fir and has since established infestations in all true firs in eastern and western North America. There are several indicators of the presence and severity of BWA, and mortality can occur rapidly or trees may persist for many decades depending on the type and intensity of infestation. Severity ratings to describe damage have largely been based on a system developed for balsam fir in Newfoundland. Modifications to this system, also developed in eastern North America, used similar characteristics, but reduced the number of classes using qualitative damage assessments. Quantitative rating systems have been developed in the western United States, however much of the research in the Pacific Northwest is based on long-term monitoring studies that describe damage patterns for host species and quantify mortality rates. Results are inconsistent geographically and between tree species, and do not incorporate stand-specific information with individual tree ratings. This emphasizes the need for a species-specific, stand-level rating system, particularly in the west where the insect is expanding its range into novel habitat, likely as a result of climatic changes. We developed a new, more comprehensive rating system for grand fir and subalpine fir in the northwest US that combines all the symptoms of BWA-related tree damage with stand-level information about species composition and structure. Our scale identifies differences between each species and quantitatively differentiates between damage classes, identifying the symptoms defining each class. This rating system allows for more efficient classification of stand-level risk for BWA and will be used to develop a predictive risk model that identifies factors that can assist land managers with damage mitigation strategies.