Linking climate, gross primary productivity, and site index across forests of the western United States

• Published in: Canadian journal of forest research Vol. 41; no. 8; pp. 1710 - 1721
• Main Authors: Weiskittel, Aaron R, Crookston, Nicholas L, Radtke, Philip J
• Format: Journal Article
• Language: English
• Published: Ottawa, ON NRC Research Press, National Research Council Canada 01.08.2011; NRC Research Press; National Research Council of Canada; Canadian Science Publishing NRC Research Press
• Subjects: Biological and medical sciences; climate; Climate change; Climate effects; Environmental aspects; Forest management; Forest productivity; Forestry; Forests; Fundamental and applied biological sciences. Psychology; Global temperature changes; growth models; moderate resolution imaging spectroradiometer; prediction; Primary production; primary productivity; site index; Sustainable forestry; United States; North America; America; United States > US; USA; Climate; Forests; Primary productivity; Site index; Forestry
• ISSN: 0045-5067; 1208-6037
• DOI: 10.1139/x11-086

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA’s MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited (R2 of 36%–56%), while the relationship between GPP and climate (R2 of 76%–91%) was stronger than the one between SI and climate (R2 of 68%–78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0–5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively.