The paper presents a comprehensive review of the biomass equations for 65 North American tree species. All equations are of the form M = a@, where M is the oven-dry weight of the biomass component of a tree (kg), D is diameter at breast height (DBH) (cm), and a and b are parameters. Equations for the following tree components were included in the review: total aboveground biomass, stem wood, stem bark, total stem (wood and bark), foliage, and branches (wood and bark). A total of 803 equations are presented with the range of DBH values of the sample, sample size, coefficient of determination R2, standard error of the estimate, fitting method used to estimate the parameters a and b, correction factor for a bias introduced by logarithmic transformation of the data, site index and geographic location of the sampled stand(s), and a reference to the paper in which the equation (or the data) was published. The review is a unique source of equations that can be used to estimate tree biomass and/or to study the variation of biomass components for a tree species. 0 1997 Elsevier Science B.V.

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The emergence of forest ecosystem management presents new information challenges for forest managers. Shifting views of the forest from primarily one as a production system for wood fibre to an ecosystem with spatially and temporally complex interrelationships is changing the demand for information about the forest. These new information needs are characterized by greater complexity, limited availability of mechanistic hypotheses, and a paucity of data. Empirical and process modelling approaches have evolved in forest management to solve different problems, and debate about the two approaches has existed for some time. Which approach to forest modelling will best be able to meet the challenges of ecosystem management? Empirical models seek principally to describe the statistical relationships among data with limited regard to an object's internal structure, rules, or behaviour. In contrast, process models seek primarily to describe data using key mechanisms or processes that determine an object's internal structure, rules, and behaviour. In addition, mechanisms included in process models are general enough that they can maintain some degree of relevance for new objects or conditions (mechanism constancy), while empirical models tend not to be tied to any specific mechanism, so that derived model parameters must remain constant (parameter constancy) for new objects or conditions. Based on these differences, we argue that process models offer significant advantages over empirical models for increasing our understanding of and predicting forest (a tree, a stand, a landscape) behaviour. Process models are, therefore, more likely to meet the information challenges presented by ecosystem management.

Critical errors exist in some methodologies applied to evaluate the effects of using forest biomass for bioenergy on atmospheric greenhouse gas emissions. The most common error is failing to consider the fate of forest carbon stocks in the absence of demand for bioenergy. Without this demand, forests will either continue to grow or will be harvested for other wood products. Our goal is to illustrate why correct accounting requires that the difference in stored forest carbon between harvest and no-harvest scenarios be accounted for when forest biomass is used for bioenergy. Among the flawed methodologies evaluated in this review, we address the rationale for accounting for the fate of forest carbon in the absence of demand for bioenergy for forests harvested on a sustained yield basis. We also discuss why the same accounting principles apply to individual stands and forest landscapes.

Recent progress toward the application of process-based models in forestmanagement includes the development of evaluation and parameter estimation methods suitable for models with causal structure, and the accumulation of data that can be used in model evaluation. The current state of the art of process modeling is discussed in the context of forest ecosystem management. We argue that the carbon balance approach is readily applicable for projecting forest yield and productivity, and review several carbon balance models for estimating stand productivity and individual tree growth and competition. We propose that to develop operational models, it is necessary to accept that all models may have both empirical and causal components at the system level. We present examples of hybrid carbon balance models and consider issues that currently require incorporation of empirical information at the system level. We review model calibration and validation methods that take account of the hybrid character of models. The operational implementation of process-based models to practical forest management is discussed. Methods of decision-making in forest management are gradually moving toward a more general, analytical approach, and it seems likely that models that include some process-oriented components will soon be used in forestry enterprises. This development is likely to run parallel with the further development of ecophysiologically based models.

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Differences in yield-density models derived from an additive experimental design were used to compare the relative competitiveness of nine early-successional boreal forest plants (aster, grass, fireweed, fern, raspberry, willow, alder, birch, and aspen) on jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana (Mill.) BSP). A randomized complete block split-split-plot design with three replications blocked on soil type was used. Initial density gradients were 0-4 plants/m2 for woody and 0-8 plants/m2 for herbaceous species. An a priori analytical approach that compared a full model (using linear regression analysis of 4th-year stem diameter of conifers under increasing cover and height of competitors) to various reduced models was used to assess competition. Increasing cover and (or) height of all competitors (except fern) significantly (P < 0.05) decreased conifer stem diameter. The final regression model (based on visual estimates of cover and differences in initial conifer size) accounted for 89% of the variation in stem diameter. During the years studied, both conifers responded similarly to competition, and herbaceous species were on average 28.9% more competitive than woody species. Under different growing conditions (e.g., a natural forest) the relative competitiveness of herbaceous and woody species may vary from these results.