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

Abstract

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.

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

“…We computed the variable importance measure of each predictor as given as the average percent change (before and after permutation) in the OOB error when the variable is permuted, while all others are retained unchanged. For model optimization, the backward variable selection approach [10] was applied minimizing the RMSE and maximizing the pseudo R 2 .…”

“…Geocentric approaches have been used as a basis for estimating SI or other measures of forest productivity and involve relating SI to various direct and/or indirect environmental factors [1]. Many studies have revealed environmental predictors of SI, using edaphic [2][3][4][5], topographic [6][7][8], and/or climatic [9][10][11] variables. Geocentrically-based (biophysical) SI models are independent from stand age and structure, usually have satisfactory prediction power and, therefore, provide tools that can effectively inform forest management.…”

Development of height to crown base models for thirteen tree species of the North American Acadian Region

“…Since site index is rarely measured in the Acadian Region and detailed soil maps do not exist for much of the region, CSI was derived based on the geographical location of sample plots. This index was based on 1-km 2 climate normals from 1960 to 1991 and an empirically derived relationship with observed site index (Weiskittel et al 2011a). We also tried the Hopkins (1938) Index to transform elevation, latitude, and longitude.…”

Potential impacts of regional climate change on site productivity of Larix olgensis plantations in northeast China

“…The site index is assumed to be constant over time, and thus included as a driving variable in forest growth and yield models. In reality, the site index often varies greatly due to changes in the genetic make-up of stands, climatic conditions and management practices (Monserud & Rehfeldt 1990, Valentine 1997, Weiskittel et al 2011). Empirical models have been developed to estimate the site index from climate, soil and vegetation information, thus allowing the possibility of predicting changes in the site index, forest growth and forest yield under climate change (McKenney & Pedlar 2003, Nigh et al 2004, Monserud et al 2006, 2008, Albert & Schmidt 2009, Aertsen et al 2011, Nothdurft et al 2012.…”