Predicting lodgepole pine site index from climatic parameters in Alberta

Abstract

We sought to evaluate the impact of climatic variables on site productivity of lodgepole pine (Pinus contorta var. latifolia Engelm.) for the province of Alberta. Climatic data were obtained from the Alberta Climate Model, which is based on 30-year normals from the provincial weather station network. Mapping methods were based on ANUSPLIN, Hutchinson's thin-plate smoothing spline in four dimensions (latitude, longitude, elevation, climatic variable). Site indices based on stem analysis (observed dominant height at an index age of 50 years at breast height) were used as a measure of forest site productivity. A total of 1145 site index plots were available for lodgepole pine, the major forest species in Alberta. Regression analyses were used to predict site index as a function of climatic variables for each plot. The strongest linear predictors of site index were growing degree days > 5 o (GDD5), the Julian date when GDD5 reaches 100 (D100), and July mean temperature (MTWM). A nonlinear model with D100 as the predictor variable was chosen as the final model. Both the observed and the predicted site indices from the 1145 locations were interpolated using ANUSPLIN and mapped using ArcView. We concluded that climate is an important component of site productivity, accounting for about one quarter of the variation in lodgepole pine site index across the province.

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

“…Using traditional growth and yield models, it is difficult to predict forest growth under climate change because the site index is always assumed to be constant (Fontes et al 2010, Mäkelä et al 2012. Although process-based models are still widely used, recent models describing the relationships between the site index and environmental/climatic factors have the potential to facilitate the prediction of timber production in the context of environmental change (Monserud et al 2006, Albert & Schmidt 2009. In this study, we developed a climate-sensitive site index model using the GAM method.…”

“…A variety of modeling approaches have been successfully applied to predict the site index from climate and other environmental variables. For instance, studies have used parametric modeling, such as multiple linear and nonlinear regressions (Chen et al 2002, McKenney & Pedlar 2003, Nigh et al 2004, Monserud et al 2006, Bravo-Oviedo et al 2007, Pinno et al 2009), as well as nonparametric modeling, such as classification and regression trees (Aertsen et al 2010, Afif-khouri et al 2011, generalized additive models (Albert & Schmidt 2009, Aertsen et al 2010, artificial neural networks and boosted regression trees (Aertsen et al 2010(Aertsen et al , 2011. After comparing the performance of five modeling techniques, Aertsen et al (2011) concluded that non-parametric models, such as Generalized Additive Models (GAM), were superior to traditional multiple linear regression in site index predictions.…”

“…They found that the site index for all species increased as the temperature rose, and that site index increased with precipitation for the two latter species. Monserud et al (2006) found that the strongest linear predictors of site index for lodgepole pine were growing with degree days > 5 °C (the Julian date when degree days > 5 °C reaches 100) and the July mean temperature. They also found that climate could explain approximately one quarter of the variation in the lodgepole pine site index within the Alberta Province.…”

“…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. The climate variables that influence the site index vary with species and sites, as do the magnitude of these variables (Messaoud & Chen 2011).…”

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

“…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.…”