Testing a generalized leaf mass estimation method for diverse tree species and climates of the continental United States

Abstract

Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available.

Keywords: allometry; biomass; foliage mass; national forest inventory; species functional traits.

FIGURE 1

Sample site locations within continental United States of all 3628 sample trees used.

FIGURE 2

Relative importance of variables for prediction of leaf mass rounded to nearest 0.01% of ordinary least squares model with diameter at breast height (DBH), specific gravity as given by Chave et al. (2009) (SG_p), mean annual temperature (MAT), mean annual precipitation (MAP), crown classification (CC), leaf longevity (LL), shade tolerance (ST), and uncompacted live crown ratio (LCR). The relative importance gives the proportion of model explained variance into nonnegative contributions from each variable based on sequential R ² but accounts for the dependence on the order the variables were added by averaging over all possible orderings (Grömping, 2006).

FIGURE 3

Probability density distribution of individual tree prediction error (%) of foliage estimation for Equations (1)–(6). The dashed vertical line is the median of the mean percentage error (MPE) distribution for each equation: Equation (1) = 9.82%, Equation (2) = 9.34%, Equation (3) = 19.83%, Equation (4) = 14.25%, Equation (5) = 15.07%, and Equation (6) = 10.01%. Positive values mean overestimation of measured leaf mass.