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Unlocking the climate riddle in forested ecosystems

Informally Refereed

Abstract

Climate information is often used as a predictor in ecological studies, where temporal averages are typically based on climate normals (30-year means) or seasonal averages. While ensemble projections of future climate forecast a higher global average annual temperature, they also predict increased climate variability. It remains to be seen whether forest ecosystems will respond more to changes in mean climate conditions or changes in climate variability. Our objective was to compare the relative importance of climate mean versus variability metrics as predictors of tree mortality and regeneration. Using the 4-km PRISM and 32-km NARR climate datasets, both mean and variability metrics were derived for Forest Inventory and Analysis (FIA) plot locations across the eastern United States. Tree mortality and seedling abundance data were obtained from FIA plots that were visited twice in the years from 2000 to 2010. A number of statistical approaches (including correlation analysis, and an algorithmic method, Random Forests) were used to examine the relative importance of mean versus variability of climate data in the context of evaluating changes in tree and seedling attributes.

Parent Publication

Keywords

statistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring

Citation

Liknes, Greg C.; Woodall, Christopher W.; Walters, Brian F.; Goeking, Sara A. 2012. Unlocking the climate riddle in forested ecosystems. In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 99-103.
https://www.fs.usda.gov/research/treesearch/42684