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Comprehensive Methods for Earlier Detection and Monitoring of Forest Decline
Author(s) -
Jennifer Pontius,
Richard A. Hallett
Publication year - 2014
Publication title -
forest science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 77
eISSN - 1938-3738
pISSN - 0015-749X
DOI - 10.5849/forsci.13-121
Subject(s) - threatened species , climate change , forest ecology , environmental resource management , context (archaeology) , stressor , environmental science , normalization (sociology) , ecology , ecosystem , geography , habitat , biology , archaeology , neuroscience , sociology , anthropology
Forested ecosystems are threatened by invasive pests, pathogens, and unusual climatic events brought about by climate change. Earlier detection of incipient forest health problems and a quantitatively rigorous assessment method is increasingly important. Here, we describe a method that is adaptable across tree species and stress agents and practical for use in the field. This approach relies on: (1) measurements covering a range of forest decline symptoms, from early decline to imminent death, (2) normalization of each measurement within each species’ natural range, and (3) combining normalized measurements into one summary decline rating, thus creating a rigorous, detailed assessment of forest condition within the context of the species’ typical characteristics. We demonstrate the utility of this approach in comparison to traditional field assessments of forest condition for both early detection and more sensitive monitoring over time. This comprehensive approach will allow researchers and forest managers to track subtle changes in tree condition over shorter periods of time, an imperative advancement for the detection and monitoring of invasive pests. While the case studies presented here are based on specific tree species and stress agents, this approach is scalable and broadly applicable to other tree species and stressors, making it a valuable approach for forest health monitoring and assessment.

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