G. L. Baskerville

Bio: G. L. Baskerville is an academic researcher. The author has contributed to research in topics: Regression analysis & Nonlinear regression. The author has an hindex of 1, co-authored 1 publications receiving 1018 citations.

Use of Logarithmic Regression in the Estimation of Plant Biomass

Abstract: The basic assumptions of regression analysis are recalled with special reference to the use of a logarithmic transformation. The limitations imposed on inference-making by failure to comply with th...

Tree allometry and improved estimation of carbon stocks and balance in tropical forests

Abstract: Tropical forests hold large stores of carbon, yet uncertainty remains regarding their quantitative contri- bution to the global carbon cycle. One approach to quantifying carbon biomass stores consists in inferring changes from long-term forest inventory plots. Regres- sion models are used to convert inventory data into an estimate of aboveground biomass (AGB). We provide a critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ‡ 5 cm diameter, directly harvested in 27 study sites across the tropics. Proportional rela- tionships between aboveground biomass and the prod- uct of wood density, trunk cross-sectional area, and total height are constructed. We also develop a regres- sion model involving wood density and stem diameter only. Our models were tested for secondary and old- growth forests, for dry, moist and wet forests, for low- land and montane forests, and for mangrove forests. The most important predictors of AGB of a tree were, in decreasing order of importance, its trunk diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). Overestimates prevailed, giving a bias of 0.5-6.5% when errors were averaged across all stands. Our regression models can be used reliably to predict aboveground tree biomass across a broad range of tropical forests. Because they are based on an unprece- dented dataset, these models should improve the quality

Improved allometric models to estimate the aboveground biomass of tropical trees

Abstract: Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter-height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter-height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development.

National scale biomass estimators for United States tree species

Abstract: Estimates of national-scale forest carbon (C) stocks and fluxes are typically based on allometric regression equations developed using dimensional analysis techniques. However, the literature is inconsistent and incomplete with respect to large-scale forest C estimation. We compiled all available diameter-based allometric regression equations for estimating total aboveground and component biomass, defined in dry weight terms, for trees in the United States. We then implemented a modified meta-analysis based on the published equations to develop a set of consistent, national-scale aboveground biomass regression equations for U.S. species. Equations for predicting biomass of tree components were developed as proportions of total aboveground biomass for hardwood and softwood groups. A comparison with recent equations used to develop large-scale biomass estimates from U.S. forest inventory data for eastern U.S. species suggests general agreement (±30%) between biomass estimates. The comparison also shows that differences in equation forms and species groupings may cause differences at small scales depending on tree size and forest species composition. This analysis represents the first major effort to compile and analyze all available biomass literature in a consistent national-scale framework. The equations developed here are used to compute the biomass estimates used by the model FORCARB to develop the U.S. C budget. FOR. SCI. 49(1):12-35.

Error propagation and scaling for tropical forest biomass estimates.

Abstract: The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. To make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimates, yet this uncertainty is rarely evaluated carefully. Here, we quantify four types of uncertainty that could lead to statistical error in AGB estimates: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. In previous studies, these sources of error were reported but rarely integrated into a consistent framework. We estimate all four terms in a 50 hectare (ha, where 1 ha = 10(4) m2) plot on Barro Colorado Island, Panama, and in a network of 1 ha plots scattered across central Panama. We find that the most important source of error is currently related to the choice of the allometric model. More work should be devoted to improving the predictive power of allometric models for biomass.