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.

The red imported fire ant, Solenopsis invicta Buren, is an invasive pest from South America that currently occupies much of the south-eastern USA. Global warming is likely to allow range expansion of many invasive species, including S. invicta . We used a dynamic, ecophysiological model of fire ant colony growth coupled with models simulating climate change to predict the potential range expansion of S. invicta in the eastern USA over the next century. The climate change scenario predicted by the Vegetation-Ecosystem Modelling and Analysis Project (VEMAP) was used in our analyses. Our predictions indicate that the habitable area for S. invicta may increase by c. 5% over the next 40 -50 years (a northward expansion of 33 ± 35 km). As the pace of global warming is expected to quicken in the latter half of the century, however, the habitable area for S. invicta in 2100 is predicted to be > 21% greater than it currently is (a northward expansion of 133 ± 68 km). Because the black imported fire ant, Solenopsis richteri Forel, occupies higher latitudes than S. invicta , the overall area of the eastern USA infested with invasive Solenopsis species could be greater than that estimated here.